Abstract: This study proposes a hybrid Simulated Annealing with solutions memory (SAM) to solve university course timetable problems. Simulated Annealing (SA) is one of the popular meta-heuristic algorithms for solving combinatorial optimization problems. However, SA could get trapped in local optimum, especially when the temperature becomes very low. In order to escape from this local optimum, this hybrid work tried to jump to another promising region using not accepted solutions saved in the memory .The computational results tested on ITC 2007 course timetabling benchmark datasets showed that SAM, can consistently produce good quality solutions, which are comparable to other approaches tested on ITC 2007 datasets.