This paper proposes a hybrid particle swarm optimization algorithm for the minimization of makespan and total flowtime in permutation flow shop scheduling problems, which combines particle swarm optimization algorithm with variable neighborhood search algorithm. The initial population is generated by the NEH constructive heuristic to enhance the quality of the initial solutions. A heuristic rule called the ranked order value (ROV) borrowed from the random key representation is developed, which apply the continuous particle swarm optimization algorithm to all classes of sequencing problems. The strategy of constructing external data set based on combining strong predominance ranking and crowding distance ranking was introduced. The global best solution was updated based on the strategy of combining Sigma method and roulette method. VNS was applied to enhance the local search for the pareto solutions. Finally, the proposed algorithm is tested on a set of standard instances taken from the literature provided by Taillard and compared with SPEA2. The computation results validate the effectiveness of the proposed algorithm.