Generally, timetabling is done by using conventional tables or spreadsheets. As a result, its affect the quality of timetable and it could drain time and energy if data is considered in thousands. Based on these problems, it requires an intelligent system that not only automates its process but also optimizes the result. Particle Swarm Optimization (PSO) is a popular metaheuristic algorithm to solve multiparameter optimization problems. Discrete PSO is used in this study because of combinatorics problems. Various strategies are also used in this method such as transposition method for particle movement, guided random strategies, and particle's position repair strategies. The strategies is expected to improve timetabling result. With the various strategies that have been used, this study will use “Hybrid Discrete PSO†approach. The test results showed the combination of parameters that resulting the best fitness are b_loc=1, b_glob=0,8, b_rand=0, number of particle is 200 and number of iteration is 40. The resulting fitness is 0,018896357 with the total execution time is 34 minutes 16 seconds 358 miliseconds.