To avoid the locally optimum which is frequently be the result of a calculation of particle swarm optimization (PSO) algorithm, it is proposed in this study a new mixed PSO algorithm with multistage disturbance (MPSO). MPSO combined features from two former classic improved PSO algorithms, which are standard particle swarm optimization (SPSO) and standard particle swarm optimization with a constriction factor (PSOCF). Furthermore, a strategy with multistage disturbances was also introduced into the algorithm:The first-level disturbance was used to enhance the ability of the particles to traverse the solution space when renewing the positions, while the second-level disturbance would be introduced when locally optimal solution was received to continue the optimization process. Six test functions, namely the Sphere, Ackley, Rastrigin, Styblinski-Tang, Duadric, and Rosenbrock functions, were used to simulate the optimization calculation, and the results from proposed algorithm MPSO were compared with those from SPSO and PSOCF. The results show that for the test functions, MPSO can get the optimal value much more quickly and easily than the other two algorithms, and the convergence precision of MPSO was significantly higher than the others. It can be concluded that MPSO can get over the problem of locally optimal solution when dealing with multimodal functions.