This thesis investigates the design of a local planning method for a reversing single joint tractor-trailer system that can be used in a sampling-based motion planner. The motion planner used is a Rapidly-exploring Random Tree (RRT) developed by Scania. The main objective of a local planning method is to generate a feasible path between two poses, which is needed when expanding the search tree in an RRT. The local planning method described in this thesis uses a set of curves, similar to Reeds-Shepp curves, feasible for a single joint tractor-trailer system. The curves are found by solving a constrained optimization problem that adheres to the kinematic model of the system. The reference for the tractor is generated by discretizing the path between curves. The reference for the trailer is generated by simulating the mission backwards where the curve radiuses are used as input. Simulating the mission backwards circumvents the instability of the system when reversing. The generated references are then compared to references generated by a lattice-based motion planner. The length of the references generated by the RRT are smaller than those generated by the lattice-based motion planner in simple open environments. The RRT had issues finding a path in cases where the environment was complex while the lattice-based motion planner found a path in every scenario. The computational time was significantly lower for the RRT in all simulations. The RRT generates all references between any two given poses while the lattice-based motion planner approximate the start and goal poses to the closest vertex in the search-space.  The references generated by the RRT did not perform optimally when small turns were needed along the curves due to the distance needed for maneuvering the tractor. Therefore, a new optimization problem has to be defined for which the small turns are considered.