The article discusses a multi-objective optimization approach for constructing an enrollment-based course timetable in universities from various sources. The model used the combination of mixed-integer and binary variables. It reaches the students' study time preferences, in which the number of the students in the same classroom is suitable for the training costs while preserving the business schedules. A compromise programming is used to transform a list of multiple objectives into a single objective function. We developed a Genetic Algorithm scheme to solve our proposed model. Finally, our proposed scheduler is evaluated with a real dataset of 3000 students. The results show the proposed method's effectiveness in both terms of solution quality and computational cost. © 2021 ACM.