As the consumption structure of residents continues to evolve, the vast market potential for cold chain logistics is set to be swiftly unlocked, leading to a rapid expansion in its scale. Given the significant energy consumption and refrigerant leakage, cold chain logistics face high carbon emissions. Addressing the stark contradiction between the accelerated expansion of cold chain logistics and the imperative for carbon emission control, exploring a balance between economic growth and environmental sustainability becomes paramount. Electric vehicles (EVs), known for their clean, green, and flexible operation, are increasingly gaining traction. Nonetheless, challenges such as expensive batteries, limited range, insufficient charging infrastructure, and extended charging durations hinder the global adoption of EVs. Additionally, the simultaneous charging of numerous EVs poses a risk to the stability of the power grid. Consequently, cold chain logistics companies opt for a mix of traditional and electric refrigerated vehicles. Moreover, without the enforcement of appropriate environmental regulations, profit-driven cold chain logistics companies are unlikely to voluntarily reduce carbon emissions due to the external nature of these emissions. Through the lens of government and market regulations, strategies for reducing carbon emissions encompass promoting new technologies, implementing effective subsidy incentives, and establishing the carbon emission market control mechanisms by setting carbon emission quota and imposing carbon tax.While many scholars have explored logistics network optimization with a mixed fleet, research on optimizing cold chain logistics networks with a heterogeneous fleet remains scarce. Furthermore, studies on carbon reduction in cold chain logistics have primarily centered on enhancing equipment energy efficiency and refining the operational and management practices of enterprises, with minimal consideration of the impact of carbon reduction policies. Although there are instances where researchers have incorporated carbon quotas, carbon taxes, or subsidies into their analyses of low-carbon cold chain logistics networks, comprehensive literature that considers the simultaneous optimization of cold chain logistics networks under the influence of these coordinated policies is lacking. This study aims to bridge this gap by focusing on the comprehensive optimization of urban low-carbon cold chain logistics networks under carbon reduction policies. It aims to simultaneously optimize the mixed fleet, delivery routes, depots, charging schedules, and time plans for client visits.Firstly, a comprehensive optimization model aimed at minimizing the total operational costs of the cold chain logistics network over a single day is introduced. By selecting a heterogeneous fleet, the proposed model accounts for varied needs for refrigerated vehicle replacements across delivery routes, ensuring a more appropriate type of vehicle is chosen for each delivery route. Furthermore, this study meticulously examines carbon emissions from both the delivery process and cold storage operations, including emissions from refrigeration units and vehicle engines, enabling precise carbon footprint calculations.Secondly, to address the complexity of the proposed model, an improved artificial fish swarm algorithm is devised, which incorporates the removal and insertion operators of the adaptive large neighborhood search. This enhancement broadens the diversity of artificial fish states and expedites the convergence speed of the algorithm.Thirdly, the improved algorithm is implemented through computational experiments involving a varied client base and is benchmarked against the numerical algorithm and the conventional artificial fish swarm algorithm to validate its effectiveness in solving the identified problem. Subsequent sensitivity analyses shed light on how the range of EVs, purchase subsidies for EVs, carbon taxes, and carbon emission quotas influence the optimization results of the proposed method.Lastly, based on sensitivity analyses, seven carbon reduction policies are suggested, focusing on vehicle technology, financial subsidies, and market mechanisms. Comparative outcomes indicate that the implementation of all policies is optimal from the perspective of goal optimization, albeit potentially being the most economically burdensome and challenging due to the involvement of different policy implementers. It is also demonstrated that the combined implementation of enhanced battery technology development and optimized purchase subsidies for EVs yields the most significant economic benefits. Concurrently, optimizing the trading price of carbon emissions alongside the carbon emission quota for cold chain logistics proves more effective over time. These findings offer a scientific basis for decision-making by competent departments of cold chain logistics in establishing reasonable carbon reduction policies.