Stochastic optimal control methods are often applied to motor control problems but have disadvantages in special situations. This paper evaluates the efficiency of sample based methods instead of stochastic optimal control methods solving motor planning problems. Standard methods such as Gibbs sampling, rejection sampling and importance sampling are applied on simple tasks. The experiments reveal specific advantages and disadvantages depending on the applied methods. Importance sampling guarantees a smooth state transition trajectory but probably violates defined boundaries. By contrast, rejection sampling generates a more scattered trajectory, however defined boundaries are not exceeded.