The differential evolution algorithm is limited in the optimization of the differential strategy, the DE/best/1 strategy has a poor global detection ability, and the weak local search ability of the DE/rand/1 strategy leads to the reduction in robustness and local optimal problems. In this study, the differential strategy is improved and the idea of neighborhood divide and conquer is added to improve the evolutionary efficiency. A differential evolution algorithm (TPSDE) based on two-stage mutation strategy with two populations is proposed. In the first stage, the advantages of the DE/best/1 strategy are employed to locally optimize the subpopulation area with completed neighborhood vector partition.In the second stage,the idea of the DE/rand/1 strategy is borrowed to achieve global optimization. Finally, the final variant individuals are obtained by weighting the vectors of the two stages, which avoids problems such as premature convergence and search stagnation. The simulation results of six test functions show that the TPSDE has significantly improved the convergence speed, optimization accuracy, and robustness.