In order to solve the defects of premature convergence, low convergence precision and slow convergence speed in the traditional DE algorithm, a multi-strategy adaptive mutation differential evolution algorithm (MsA-DE) is proposed. The algorithm combines three kinds of mutation strategies, randomly assigns the proportion, and increases the diversity of the population. By introducing the evolution threshold, the most appropriate mutation strategy is adaptively selected, and the global search and local search ability of the algorithm are balanced. Individuals crossing the boundary are treated to ensure the diversity and effectiveness of the population. Adding a perturbation mechanism improves the ability of the algorithm to jump out of local optimum, and at the same time improves the accuracy of obtaining the optimal solution. The algorithm is applied to the optimization of 14 test functions. The results show that the MsA-DE algorithm has higher convergence precision and the ability to jump out of local optimum, compared with the other three algorithms. The algorithm is applied to the capacity optimization problem of Railway Power Conditioner (RPC). The results show that the algorithm can reduce the capacity of the RPC compensation device and improve the economics of the device.