The uncertainty in environment perception is one of the challenges that we face in trajectory planning. For autonomous vehicle to be efficient, they need to be able to deal with this kind of uncertainty. In this work, we combine two existing frameworks: the Belief Functions to build evidential occupancy grid and clothoid tentacles for trajectory planning. First, we use evidential grids to represent the environment and the uncertainties which arise from ignorance and errors during the perception process. Secondly, we generate a set of clothoid tentacles in the egocentered reference frame related to the ego-vehicle, those tentacles represent possible local trajectories. Thirdly, we modify the evidential grid in order to take into consideration some traffic rules such as safety distance between vehicles. Then to choose the best tentacle to execute, we use reward system of a Markov Decision Process-like model to evaluate generated tentacles regarding several criteria including uncertainty represented by the evidential grid. Real and simulated data were used to validate the planning algorithm with evidential grids.