Abstract: With the growing global awareness of environmental protection, mining transportation is actively transforming itself, seeking greener and more economical transportation solutions to reduce negative impacts on the environment. Pure electric mining trucks, with their zero-emission and low operating cost advantages, have become the preferred solution to promote sustainable development. However, this transition also brings new challenges, especially in the construction and operation of charging infrastructure. At present, there are a series of problems in charging electric mining trucks in open-pit mines, such as the single running time is too short and the charging time is long; the charging scheduling is unreasonable, and trucks queue up at the charging station; the charging station is far away from the site, which can’t fully satisfy the charging demand of trucks, and so on. In response to these problems analyzes in depth the influencing factors of the electric mining truck charging facility planning problems, summarizes the relevant basic theories of its site selection and layout, and establishes a triple-objective mathematical model that simultaneously reduces the construction cost of charging station, driving cost, and the total distance of charging, and improves the utilization rate of charging station and meets the charging demand of trucks, which is capable of maximally improving the charging efficiency, and realizing the whole life cycle of charging cost minimization. An improved multi-modal multi-objective optimization (MMOEA_DC_HR) is proposed to optimize the charging station location, size and station-pile ratio, which retains the optimal solution of each objective and ensures the balance of the three-objective solutions. MMOEA_DC_HR clusters in dual space separately to find the correct neighborhood relationship, which effectively ensures the spatial diversity and uniformity of the distribution; A local convergence mechanism is used to protect all locally optimal solutions, ensuring that the obtained Pareto front is both uniformly distributed and maintains the diversity of key solutions to achieve the optimal combination of objective values. Finally, the validity of the model and algorithm is further verified through example applications and comparative analysis of the economic benefits of charging station construction.