For the multi-UAV charging planning under the wireless rechargeable sensor network, only considering the flight distance of the UAV to plan the optimal charging path for the cost target is single one-sided. Now the UAV flight distance, energy consumption, time cost, and UAV matching cost are combined into a new cost target model. To reduce the number of flight stops, a regular hexagonal charging model is also added, and an improved marine predator algorithm (BMPA) is proposed to be applied to this scenario. The improvement is as follows. On one hand, beetle antennae search algorithm is introduced into the marine predator algorithm to find the point with the largest odor value, which improves the optimal solution quality. On the other hand, a new adaptive nonlinear moving step parameter is added to the marine predator algorithm. As a result, the balance of exploration and development, and the global search ability are improved, and the rapid convergence of local research is promoted. The simulation results show that the proposed algorithm not only effectively reduces the number of flights, but also decreases the flight distance and computing power consumption. In addition, the new cost objective function values are reduced by 50.90%, 4.85%, and 14.38% compared with BAS, MPA, and PreWBAS algorithms, which proves the effectiveness of the improved algorithm.