To deal with the problem of premature convergence and low precision of the traditional particle swarm optimization algorithm, a particle swarm optimization (PSO) algorithm based on multi-scale cooperative mutation, is proposed, which is guaranteed to converge to the global optimal solution with probability one. The special multi-scale Gaussian mutation operators are introduced to make the particles explore the search space more efficiently. The large-scale mutation operators can be utilized to quickly locate the global optimal space during early evolution. The small-scale mutation operators, which are gradually reduced according to the change of the fitness value can implement the accuracy of the solution at the late evolution. The proposed method is applied to six typical complex function optimization problems, and the comparison of the performance of the proposed method with other PSO algorithms is experimented. The results show that the proposed method can effectively speed up the convergence and improve the stability.