Abstract: New implementation of genetic algorithms (GAs) is developed for machine scheduling problem. Machine scheduling problem is abundant among modern manufacturing system. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we proposed a hybrid genetic algorithms approach is deal with in order to adjust the crossover probability and mutation probability by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the proposed GAs method.