In order to improve the distribution and convergence of the non-dominated solution set of a multi-objective optimization problem, according to the characteristics of different differential evolution strategies, a novel decomposition multi-objective evolution algorithm based on the Chebyshev decomposition mechanism and differential evolution model with multi-strategy, namely MOEA/D-WMSDE is proposed in this paper. The MOEA/D-WMSDE uses the Chebyshev decomposition mechanism to transform the multi-objective optimization problem into a series of single objective optimization subproblems. Then the wavelet basis function and normal distribution are used to control parameters. An optimal mutation strategy based on complementary advantages of five mutation strategies is deeply studied in order to propose a new differential evolution(WMSDE) algorithm with multi-strategy. On this basis, the MOEA/D-WMSDE algorithm is realized. Finally, the ZDT and DTLZ benchmark functions are used to prove the optimization performance of the MOEA/D-WMSDE. The experimental results show that the MOEA/D-WMSDE has greatly improved the convergence and distribution, and can effectively solve the multi-objective optimization problem. Compared with the other algorithms, the overall quality of the obtained solution set is superior, which provides a new method to solve multi-objective optimization problems.