A Hybrid Bird Swarm Algorithm （HBSA） was proposed to minimize the makespan more efficiently for Permutation Flowshop Scheduling Problem （PFSP）. Firstly， to improve the quality and diversity of initial population， a new population initialization method was put forward by combining a NEH （Nawaz-Enscore-Ham） based heuristic algorithm and chaotic mapping. Secondly， to deal with the discrete scheduling problem by the algorithm， the Largest Ranked Value （LRV） rule was adopted to convert continuous position values to discrete job permutation. Finally， to enhance the ability of the algorithm to explore the solution space， local search methods for the individual best job permutation and population best job permutation were proposed on the basis of the ideas of Variable Neighborhood Search （VNS） and Iterative Greedy （IG） algorithms respectively. The proposed algorithm was simulated and tested on the widely used benchmark test set Rec and compared with Hybrid Differential Evolution algorithm proposed by Liu et al （L-HDE） algorithm， Hybrid Symbiotic Organisms Search （HSOS） algorithm， Discrete Wolf Pack Algorithm （DWPA） and Multi-Class Teaching-Learning-Based Optimization （MCTLBO） algorithm， which are the effective meta-heuristic algorithms for PFSP. The results show that the average values of Best Relative Error （BRE） and Average Relative Error （ARE） achieved by HBSA are at least 73.3% and 76.8% lower than those of the above four algorithms， thus proving that HBSA has stronger search ability and better stability. It is worth mentioning that， for Rec25 and Rec27 test instances， only HBSA achieves the currently known optimal solutions， which further proves its superiority.