Academic scheduling in schools is a difficult task normally done manually; this often creates scheduling conflicts and unequal workloads for teachers. Inefficient use of resources. This paper designs an Automatic Timetable Generator using a Genetic Algorithm, taking instructor availability, course credits, room capacity, and requirements based on a student&s;s cohort into consideration, to solve multi-constraint scheduling difficulties. This method represents timetable slots as chromosomes, commonly tests their fitness with respect to the minimization of conflicts, and crossover and mutation are frequently used to move toward an ideal solution. When tested for a sample of 20 teachers, 48 courses, 12 classrooms, and 5 working days, the generated complete schedule averaged 32.6 seconds. A. The created method demonstrated a 72% reduction in conflicts, a 40% increase in task balance, and a notable increase in classroom usage efficiency when compared to manual scheduling. Python shall be used in the system&s;s development. For back-end optimization, use Flask; for real-time visualization, use HTML, CSS, and JavaScript. The findings demonstrate that the GA-based method offers scalable, conflict-free, and repeatable schedules to educational institutions that effectively enhance scheduling accuracy and operational effectiveness.