The grey wolf optimization algorithm is an emerging swarm intelligence optimization algorithm, which has been widely applied in many fields. In order to solve its shortcomings in solving complex optimization problems, such as low precision and easy to fall into local optimum, an improved grey wolf optimization algorithm based on multi-strategy fusion is proposed. The algorithm first uses the good point set method to uniformly initialize the grey wolf population to lay the foundation for the global search diversity; Then through the mutation, crossover, and selection operations of the differential evolution algorithm, the diversity of the population is maintained, and the solution accuracy and optimization of the algorithm are improved; Finally, a nonlinear control parameter strategy is designed to coordinate the global exploration and local development capabilities of the algorithm, and a segmented step size update strategy is used to avoid the algorithm from falling into local optimum.In order to verify the effectiveness of the algorithm, 10 standard test functions are selected for simulation experiments. The experimental results show that the improved grey wolf optimization algorithm is obviously better than other comparison algorithms in terms of solution accuracy and convergence speed. It is concluded that the multi-strategy fusion effectively improves the performance of the grey wolf optimization algorithm.