<![CDATA[The scheduling process in higher education is a complex challenge because it involves time allocation, space management, and human resources. In the UNISNU Jepara Information Systems Study Program, problems such as schedule clashes, uneven distribution of lecturer workloads, and inefficient classroom utilization often occur due to the increasing number of students and variety of courses. This research proposes the application of genetic algorithm as an optimization solution due to its ability to handle problems with various constraints and produce near-optimal solutions through the process of selection, crossover, and mutation. This research includes three main stages: data collection, genetic algorithm implementation, and result evaluation. Data was obtained from academic administration documents, including class schedules, course instructors, and classroom capacity. The evaluation results show that the genetic algorithm is able to reduce schedule conflicts, improve lecturer time efficiency, and maximize the use of classrooms. In conclusion, the application of genetic algorithms not only solves technical problems in scheduling, but also contributes to the development of a modern and adaptive academic information system, supports more effective decision-making, and ensures a smoother teaching-learning process in a college environment.]]>
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