Motor planning algorithms are essential for the development of robust autonomous robot systems. Various approaches exist to compute movement trajectories efficiently by applying quadratic control costs. However, with quadratic costs hard constraints cannot be adequately modelled. In this thesis I choose the Monte Carlo (MC) sampling approach to investigate how dynamic motor planning tasks, considering hard constraints can be solved efficiently. For efficient sampling, Gibbs sampling, rejection sampling, and importance sampling are combined. Two different sampling methods are investigated. The first and simpler method does not consider the dynamic state transition model of a robot. The second method is more sophisticated and considers a linearised approximation of this dynamic model. The experiments range from simple tasks on a 2-link robot arm to tasks using a more complex 4-link robot arm. To enhance the performance of the investigated methods, they are extended by a via point approach. Finally, in a novel trajectory mixing approach complex planning scenarios are solved by mixing multiple trajectories, which are computed in parallel.