Autonomous driving is an emerging technology that is advancing in a very fast way. It is a complex challenge that involves many sections with plenty of different disciplines. One of the more important parts is trajectory planning, where this thesis it has been focused. This project revises the different algorithms of trajectory planning that have been proposed for autonomous cars. The reason why a trajectory planner based on numerical optimization algorithm such that Model Predictive Control (MPC) is proposed is also discussed. The main advantages are the possibility of generating the planning online allowing the replanning if unexpected events occurs (objects in the middle of the road, pedestrians appearing unexpectedly, etc.) and the facility of including several objectives in the optimization problem. This thesis studies different parameter that can define an optimal generated trajectory and how it is structured in the optimization program. Moreover there are several weights that should be tuned to orientate the trajectory planner in the direction that it is desired. All this tuning process is explained providing guidelines on how can be done for future cases. Finally, several testing results were included that are obtained with different parameters and structures of the program. These results are analysed and some conclusions of the efficiency of the MPC-based planning algorithm are obtained highlighting the advantages that it presents.