Abstract: A dynamic double-population particle swarm optimization (DPSO) algorithm is presented to solve the problem that the standard PSO algorithm is easy to fall into a locally optimized point, where the population is divided into two sub-populations varying with their own evolutionary learning strategies and the information exchange between them. The algorithm thus improves its solvability for global optimization to avoid effectively the precocious convergence. Then, an ordering algorithm based on DPSO is integrated with the heuristic assignation (HA) algorithm to form a new algorithm DPSO-HA so as to solve the flexible job-shop scheduling problem (FJSP). The new algorithm is applied to a set of benchmark problems as instances, and the simulation results show the effectiveness and feasibility of DPSO-HA algorithm for the flexible job-shop scheduling.