In the optimization algorithm based on genetic algorithm (GA), the performance of the algorithm depends on the GA operators used in the algorithm. Thus, in the past years many types of operators (mutation and crossover methods) which are appropriate for the respective problems have been proposed. In this paper, an optimization algorithm using several types of GA operators is proposed. In the proposed algorithm, the effectiveness of each operator used in the generation is evaluated and the probability of using that operator is changed as the generation progresses. Therefore, when a problem is given, the selection of the operators suitable for the problem is no longer needed. Furthermore, new operators can be easily added to the algorithm without discussing the effectiveness of the operator. A scheduling system using the proposed algorithm is developed, and it is applied to the scheduling problem of a parallel-unit process. The result shows that the scheduling system using the proposed algorithm can derive reasonably good schedules, even if the type of objective function is changed.