<![CDATA[Course scheduling is one of the administrative activities that has a high level of complexity because it involves constraints such as limited learning hours, the number of teachers, subject allocations, and classroom capacity. SMA Negeri 1 Bungku Selatan still uses a manual method in preparing class schedules. This often leads to inefficiencies, such as teaching schedule conflicts and an uneven distribution of teaching workloads. This study implements a Genetic Algorithm (GA) as an evolutionary computation approach to optimize the scheduling process. The research method includes requirement analysis, system design, algorithm implementation using Python, and performance testing of the generated schedule. Data were collected through observation, interviews, and documentation obtained from the school. The results of the study show that the Genetic Algorithm is capable of producing valid schedules according to school regulations, free from conflicts between teachers and subjects, and providing a more balanced distribution of teaching workloads. The algorithm can achieve optimal results in fewer than 100 generations with an average execution time of under one minute.]]>
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