A motion planner for mobile robots is commonly built out of a number of algorithms that solve the two steps of motion planning: 1) representing the robot and its environment and 2) searching a path through the represented environment. However, the available literature on motion planning lacks a generic methodology to arrive at a combination of representations and search algorithm classes for a practical application. This article presents a method to select appropriate algorithm classes that solve both the steps of motion planning and to select a suitable approach to combine those algorithm classes. The method is verified by comparing its outcome with three different motion planners that have been successfully applied on robots in practice