This paper presents an automated scheduling approach for constructing efficient and conflict-free timetables in educational institutions by leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Timetable scheduling is a complex, NP-hard problem due to numerous conflicting constraints, such as instructor availability, room capacity, and course requirements, which render manual solutions both time-consuming and suboptimal. NSGA-II, a well-known evolutionary algorithm, is employed to solve the multi-objective optimization problem by balancing conflicting goals like minimizing overlaps and optimizing resource allocation. The algorithm utilizes population-based search methods and non-dominated sorting to derive a Pareto front that allows stakeholders to select the best-fit timetable from a range of nearoptimal solutions. In this study, multiple objective functions have been defined to capture the various aspects of timetable quality, including minimized gaps in faculty schedules and improved classroom utilization. The experimental results indicate that NSGA-II produces significantly better timetables in terms of reduced conflicts and efficient resource use compared to traditional heuristic-based methods. The proposed system also offers flexibility for institutions to prioritize objectives dynamically, depending on real-time requirements, which enhances the adaptability of the timetable generation process. Furthermore, the study provides a detailed analysis of the tradeoffs involved in optimizing multiple conflicting objectives, which is critical for understanding the operational implications of different scheduling choices. The effectiveness of NSGA-II in managing timetable complexities suggests its potential as a viable and practical tool for educational scheduling applications. This approach not only saves administrative time but also ensures a fairer and more efficient use of institutional resources, ultimately contributing to an improved educational experience. Future work will focus on incorporating real-time adaptability into the scheduler by integrating IoT data feeds and further enhancing the algorithm’s computational efficiency for larger datasets