The problem of passengers transportation in a public transportation network is a complex problem. One of the segments of the problem of passengers transportation is determining the timetable for vehicles in a public transportation network. For determining the best timetable, it is unavoidable to consider the requirements and satisfaction of passengers as well as requirements of transportation companies. In recent years, the PT problem has been solved in the literature with different perspectives on the passenger requirements and transportation companies’ requirements. In this PhD dissertation, a model of the PT timetabling problem, which takes into consideration the passenger waiting time (PWT) at the stations and the vehicles occupancy ratio (VOR) is proposed. The solution aims to minimize PWT and maximize VOR. The results of the proposed method are compared to similar results from the existing literature. In addition to the review of the existing methods, the proposed optimization model for solving vehicles scheduling process based on multi-objective optimization is described in detail. An appropriate procedure for creating a timetable and vehicles schedule in public transportation is proposed. Timetabling and vehicle scheduling procedures in PT network are based on a heuristic algorithm. Due to the large search space of the problem, a multi-objective particle swarm optimization (MOPSO) algorithm is used to arrive at the solution of the problem. The solution of the problem is a set of optimal solutions, i.e. Pareto front, from which it is necessary to select one preferred optimal solution. Since the selection of one optimal solution is a time consuming process, a more appropriate method for determining the preferred optimal solution is proposed. The proposed method is compared to other two methods for solving the particular problem and it is concluded that the proposed method is more appropriate. For the purpose of evaluating the obtained solutions, a method based on qualitative analysis is proposed. The proposed multi-objective optimization method shows the advantages over existing methods regarding selection of the objective functions, defined constraints and real-time execution, which is extremely important because of the dynamics of the entire PT. 94 Further research will involve verification of the model using data from a real network scenario. It is also plan to take into consideration boarding and alighting times, transfer stations and scheduling several lines. Furthermore, it is plan to improve the existing multi-criteria optimization algorithm in favor of the stability of the algorithm by adjusting parameters of the proposed algorithm. Based on the presented results, a multi-objective model solved by the proposed algorithm for determining an optimal timetable and vehicle schedule, with the proposed method for determining the preferred optimal solution from the set of optimal solutions, confirms the advantage over existing models in the scientific literature and it is appropriate for scientists and experts in the field of public transportation.