Scheduling university courses is a complex task that balances classroom availability, instructor schedules, and course prerequisites to meet student needs in creating an efficient timetable. Traditional manual methods for scheduling are prone to errors and inefficiencies, often resulting in conflicts such as double-booked classrooms, overlapping schedules, and underutilized resources. This study aims to develop a genetic algorithm-based approach to automate and optimize the scheduling process. The proposed method utilizes a genetic algorithm that evolves a population of possible solutions through selection, crossover, and mutation, seeking to minimize scheduling conflicts and maximize the use of classroom and time slot resources. The algorithm considers both hard constraints, such as instructor and room availability, and soft constraints, like class size and instructor preferences. Results show that the algorithm effectively generates conflict-free timetables with minimal iterations. Classroom and instructor visualizations confirm the solution’s success, though further refinement is needed to optimize lab scheduling.