Abstract: Aiming at the limitation of the traditional flexible job shop scheduling problem that only considered the processing process, a scheduling model with multiple constraints such as the transportation time, delivery time, processing time and workpiece arrival time was constructed, which took maximum machine efficiency and minimum completion time as the objective. The machine efficiency began with each machine to finish machining of free time and to represent. The model fully considered multiple time factors and determined the available time period of the machine and the earliest start processing time of the workpiece through the immediately preceding process of the workpiece and the preceding process of the machine. Based on genetic algorithm, the segment coding and insertion decoding strategies were designed. The S-adaptive probability was used to improve the chromosome crossover. A selection strategy based on maximizing the efficiency of the machine was designed to mutate the machine part. In order to ensure the diversity of offspring, a local population expansion strategy was applied to expand the population. Finally, two flexible job shop scheduling problems with different scales were designed to test the model and algorithm. The experiment showed that the model was effective to solve the flexible job shop scheduling problem with multiple time and machine efficiency constraints and the algorithm got a better performance than the other selected methods.