To solve the existing problems, which include inconvenience of battery swapping for Electric Vehicle (EV) users, low utilization of battery packs in Battery Swapping Station (BSS), high charging cost, and deterioration of loading characteristics of distribution network, a space-time bi-level charging optimization model, which gives consideration to the tripartite benefits of EV users, BSS and power grid corporation, is established. The model adopts double space-time decoupling structure. The upper model, which aims at meeting the individualized needs of EV users, focuses on solving the problem of BSS selection in spatial scale; the lower model adopts a two-stage optimization strategy in time scale. The first-stage, which takes the minimized charging cost as the objective, focuses on the establishment of the battery packs charging scheme; the second-stage, which gives consideration to the incentive given by grid corporation and aims at the minimum load fluctuation and peak-valley difference of the distribution network, focuses on the optimization of the charging scheme. Finally, the Monte Carlo method is used to simulate the battery swapping demand of EV users, and a Genetic Algorithm (GA) - Particle Swarm Optimization (PSO) method is used to solve the proposed space-time bi-level optimization model. Taking a typical urban area as an example, the validity of the proposed model and method is verified by simulation. This work is supported by Science and Technology Project of State Grid Corporation of China (No. SGGSKY00FJJS1700318) andScience and Technology Support Project of Xinjiang Uygur Autonomous Region (No. 2017E0277).