Timetabling problems are often difficult and time-consuming task. It involves a set of timeslots, classrooms, subjects, students and lecturers.  The complexity problem is the constraints that exist within the resources. Thus, a technique that can handle constraints is needed to optimize the problem. Various approaches have been reported in the literature on solving university timetabling problem. This paper focuses on developing a hybrid algorithm consisting  of  a  particle  swarm  optimization  and  constraint-based  reasoning  in  solving university  timetabling  problem  in  generating  a  feasible  and  near-optimal  solution.  The proposed algorithm is tested using real data from the Faculty of Computer Science and Information System, Universiti Teknologi Malaysia. The result is compared against standard particle swarm optimization and hybrid particle swarm optimization-local search. It shows that the proposed method has outperformed others.