When a genetic algorithm is used to solve job shop scheduling, in order to obtain the optimal solution and increase the convergence speed of the algorithm, we propose an improved genetic algorithm in this study. The goal of the algorithm is to minimize the maximum completion time. First, the population size is doubled during the initialization to increase the diversity of the population and a new fitness function is adopted to make chromosome distinguishing easier in the iteration. Then, chromosomes are selected via roulette. Furthermore, crossover is completed by Precedence Operation Crossover (POX) and mutation by Reciprocal Exchange Mutation (REM). Finally, the optimization ability and convergence speed of the proposed algorithm are improved by adjusting the crossover and mutation probability with self-regulation. The simulation results show that the improved genetic algorithm has faster convergence, stronger optimization ability, and better optimal solution than the traditional one and thus it is more suitable for the processing and production in job shops.