Aiming at the current problems such as the trade-off between cost and service level in the location selection of urban electric vehicle charging stations, and the fact that coverage is not used as a rigid constraint, this paper proposes a site selection model that integrates dual-objective optimization and coverage constraints, and designs the Pareto optimal solution set with the improved whale optimization algorithm (IWOA). Firstly, the coverage rate is set as a rigid constraint, and a dual-objective programming model with maximizing service level and minimizing the total cost of the whole cycle is established, which uses the cosine-based distance satisfaction function and M/M/s queuing model to quantify the service level, and evaluates the cost by integrating fast/slow charging preferences and peak hour correction by capital recovery factors. Secondly, adaptive weight control, hybrid search strategy and local search enhancement are incorporated into the algorithm to improve its global search ability and convergence efficiency. Finally, based on the actual data of Xi'an, 80 demand points and 64 candidate sites are selected, and the performance of the IWOA and other algorithms is compared through example analysis, and a cost-service-level-coverage three-dimensional decision-making framework is constructed to show the influence of constraints on the optimization goal. The results show that the proposed method achieves the optimal trade-off when the coverage constraint is 0.8, the service coverage rate increases to 93.6 ± 1.7%, the full-cycle cost is reduced to 12.24 ± 0.73 million yuan, and the average queue waiting time of users is shortened to 3.3 ± 0.4 minutes.