To address the issues related to the construction of battery swapping stations during the development and popularization process of new energy battery-swapping vehicles, an optimization mathematical model with the goal of minimizing the average annual comprehensive cost over the operational target years of the swapping stations was established. The model comprehensively considered factors such as land price, construction cost, operational cost, maintenance cost, road traffic flow, and service capacity, with battery swapping capacity and distance as constraints. Additionally, an improved quantum particle swarm optimization algorithm was utilized to solve the model. The algorithm introduced an adaptive adjustment of inertia weight to enhance the overall search capability of the particles. It employed Logistic chaotic mapping to initialize population information, improving the populations travers ability. Through Levy flight strategy and Cauchy mutation strategy, the diversity of the population was increased, and the search space in the iteration process was expanded, further enhancing the global search capability of the algorithm and enabling it to quickly escape from local optima. This algorithm is applied to the actual planning of Kuancheng District of Changchun City, and the relevant data of this area is introduced into the established mathematical model. It is determined that the construction of four power conversion stations in this area meets the construction objectives. Meanwhile, the construction location and capacity of each power station are determined, which proves the feasibility and practicability of the method mentioned.