This paper addresses the operation of electric mining trucks (EMTS) and dynamic battery swapping scheduling in open-pit mines, proposing a multi-objective collaborative optimization framework based on Discrete Event Simulation (DES). In response to the limitations of traditional methods in handling the dynamic environment of mining areas and complex constraints, such as queuing behavior and multi-resource dynamic coupling, this study employs a DES model to simulate the fleet’s operation in the mining area, accurately capturing the dynamic interactions between vehicles and facilities. The optimization objectives are set to maximize ore production and minimize battery swapping station queue times. The Non-dominated Sorting Genetic Algorithm II NSGA-II algorithm, combined with DES, is applied with parallel simulation and repair mechanisms to rapidly solve high-quality scheduling solutions. Additionally, a rolling horizon optimization strategy is introduced to effectively address dynamic disturbances in the mining area. Based on real data from a large open-pit mine in Nei Monggol, China, experimental results show that the proposed method reduces battery swapping queue time from 16.04 min to 8.55 min, achieving a 46.7% reduction for a 17-vehicle fleet. When extended to a 40-vehicle large-scale fleet, the queue time is further reduced from 24.98 min to 13.67 min through parameter optimization, resulting in a 45.3% reduction. The study demonstrates the superior engineering applicability and robustness of the proposed framework, providing an efficient scheduling solution for the electrification transformation of open-pit mining.