University class scheduling problem is one of the most important and complex issues in the academic field. This problem is recognized as one of the NP-HARD issues due to its various limitations. On the contrary, genetic algorithms are commonly used to solve NP-HARD problems, which is one of the decision-making problems and is basically one of the most fundamental classes of complexity. The university course planning includes severe constraints such as classroom, classroom curriculum, and faculty. At the same time, some soft constraints should be considered, such as student and faculty preferences and favorite class time. In this research, as a novel contribution, an integer model for scheduling university classes is presented. In this model, the preferences of professors and students are in accordance with the satisfaction values obtained through questionnaires. Moreover, a genetic algorithm has been developed to solve the model. The results show that the classroom timeline by this algorithm goes well during each run. Moreover, considering an exploratory search for the genetic algorithm can greatly improve the performance of this algorithm.

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