<![CDATA[Scheduling is an important issue in the implementation of activities, so that absence of such activities will not run smoothly. One example of scheduling is the scheduling of semester exam is performed on a STMIK Kadiri. Scheduling tests done still manually (conventional) so it may take longer computation. This is because the difficulty of putting slots schedule to avoid clashing occurs and there are lots of class but the test room which can be used a bit. So it needs optimization scheduling that is able to minimize conflicting schedules and activities the test can run well. Genetic algorithm is one of the most common optimization methods is used to solve the problems of scheduling. The data used in this study using the test schedule data will be represented in chromosomes, in the form of code exam schedule. Crossover method used is onecut point while mutase method using reciporal exchange mutation and elitism selection method and roulette wheel. The optimal parameter values ​​obtained based on the test result are population size 60, generation size as much as 850, with cr and mr value is 0,5 and 0,5. So the fitness value that is gained is 0.000574..]]>
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