Universities, as major energy consumers, play a key role in reducing energy consumption through intelligent planning. Optimizing course scheduling is one of the less explored but highly impactful solutions for reducing energy use in educational buildings. This study aims to develop an energy-efficient class scheduling model that considers temporal, spatial, and infrastructural constraints of academic campuses. The proposed model formulates the scheduling process by incorporating constraints such as building capacity, simultaneous sessions, professor availability, and the need for symmetrical timetables. Unlike conventional scheduling, which is typically based on administrative convenience, this approach leverages real meteorological data (temperature, humidity, wind speed) from the fall semester in Tehran to align class times with periods of lower energy demand. The innovation of this study lies in the integration of energy-saving goals with scheduling algorithms, creating a dual-objective framework that balances academic and environmental priorities. While many studies have addressed either scheduling or energy management, few have proposed an integrated, constraint-based model tailored for postgraduate academic settings. The results show that applying the optimized scheduling model leads to a significant reduction in energy consumption, between 8% to 12% depending on the scheduling scenario. Additionally, the spatial-temporal optimization allows for better use of daylight and ventilation potential, contributing to improved energy performance of buildings without requiring any physical retrofitting.