To solve the permutation flowshop problem more effectively, a novel artificial immune particle swarm optimization (PSO) algorithm has been proposed. The new algorithm combined the biology immune system theory with particle swarm algorithm by the following phases. Firstly, the scheduling objective and constrain condition were served as antibodies while solutions was served as antigens. Secondly, the particles were encoded as workpiece processing sequence. Furthermore, a concentration selection strategy was adopted to maintain the particle diversity. Finally, comparing with genetic algorithm and PSO, case results showed that immune PSO algorithm not only optimized results and convergence velocity but also had a small fluctuation.