The increasing number of electric vehicles (EVs) and the enhancement of their range capabilities have made the coupling between EV traffic networks and power grids more complex. Describing accurately the charging demand under such complex coupling scenarios, and balancing the interests of charging station operators and EV users, is a crucial consideration in EV charging station planning. When users have a charging demand, and there are multiple charging stations within the remaining range, they consider various influencing factors and ultimately choose the charging option with the minimum regret value. To address this, we first establish a dynamic charging demand model for electric vehicles considering inter-station interaction effects to more accurately calculate the charging demand. Then, we consider a bi-level optimization model for charging station planning that balances the interests of both charging station operators and electric vehicle users. We solve this multi-objective function using the slime mold algorithm. Finally, using a coupled network composed of a 22-node highway network and the IEEE 33-node power grid as an example, we compare the results to validate that the proposed planning method preserves the spatiotemporal distribution characteristics of EV charging demand while benefiting both charging stations and users.