<![CDATA[The process of drafting schedules manually felt less efficient because it takes a long time. The problem of drafting the schedule will be complex if the number of components is more large amount of data from each component. The expected schedule is not just a schedule that does not clash, but a schedule that can adapt to some constraints that must be met within the schedule. Genetic Algorithms are algorithms that are iterative, self-adjusting and probabilistic algorithms in search for global optimization. The process of chromosome initialization generated from teacher assignment data by integer representation of each gene containing randomly generated assignment codes. Each chromosome with the highest fitness value is a representation of the subject schedule solution. From the testing process that has been done, has obtained the parameters of Genetic Algorithm is the best population number is 90, the value of the combination of Cr and Mr is 0.5 and 0.5, and the number of generations as much as 40000. The process of finding solutions using these parameters obtained the value of fitness that is 0,8451.]]>
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