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In this paper, we present a Knowledge Based Genetic Algorithm (KBGA) for the scheduling of Flexible manufacturing system. The proposed algorithm integrates the knowledge base for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to and large number of generations. This paper extends the simple genetic algorithm and proposes a new methodology to handle a complex variety of variables in a typical FMS problem. To achieve this aim, three new genetic operators--knowledge based: initialization, selection, crossover, and mutation are introduced. The methodology developed here helps to improve the performance of classical GA by obtaining the results in fewer generations. [ABSTRACT FROM AUTHOR]