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Job shop scheduling plays an important role in improving production efficiency and reducing production cost for manufacturing enterprises, aiming at the shortcomings of a single opti-mization algorithm in solving scheduling optimization problems, this paper explores the balance be-tween solution speed and solution quality, and proposes a hybrid firefly and particle swarm optimi-zation algorithm with multi-scale cooperative variation; The dynamic adaptive strategy is introducedto divide the population into two groups, and the parallel evolution of the two groups can improv-e the solution speed while maintaining the diversity of the population; The multi-scale cooperative mutation operator is introduced, and the adaptive Gaussian mutation mechanism with different vari-ances is used to make the population search the solution space with the most dispersed mutation scale, and the local retrieval ability of the algorithm is further improved by initializing the popula-tion through chaos; The proposed algorithm is applied to function optimization and flow shop sch-eduling, the experimental results show that the algorithm is superior to the comparison algorithm in solving efficiency and accuracy, and has good performance and application value.