This study focuses on the resource scheduling problem of the precooling service platform, aiming to provide technical methods for the post-harvest precooling issue of smallholders in China. Considering the time sensitivity of precooling demands and the cost-effectiveness of service operations, and incorporating both fixed precooling and mobile precooling resources, this problem is formulated as a multi-depot vehicle routing problem with heterogeneous service efficiencies and time windows. A mixed-integer linear programming multi-objective optimization model is formulated to minimize the scheduling cost of precooling services while also reducing precooling delay times. Furthermore, considering the heterogeneity of precooling resources, an EC-ALNS multi-objective optimization algorithm, based on an enhanced Box splitting method and an adaptive large neighborhood search algorithm, is developed to efficiently obtain an approximately accurate Pareto frontier for this problem. The effectiveness and advantages of the EC-ALNS are verified through comparisons with the CPLEX solver and two classical algorithms. Finally, a case study is conducted to validate the robustness of our model, and management implications are derived through numerical experiments and parameter sensitivity analysis under various order scenarios.