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Particle swarm optimization (PSO) is a population-based stochastic algorithm designed to solve complex optimization problems such as the Flexible Job Shop Scheduling Problem (FJSP). As a metaheuristic, the performance of the PSO is heavily affected by two elements: the size of the search-space and the way of its exploration. In this paper, we present a specific PSO algorithm for the FJSP that use Lower-bounds to bypass regions not containing optimal solutions. The proposed algorithm is a two-level PSO. The upper-level handles the mapping of operations to machines while the lower-level handles the ordering of operations. The performance gain in terms of solution optimality and CPU time, obtained by our method, has been validated by external FJSP benchmarks.