This paper addresses the simultaneous design and path planning problem, in which features associated to the bodies of a mobile system have to be selected to find the best design that optimizes its motion between two given configurations. Solving individual path planning problems for all possible designs and selecting the best result would be a straightforward approach for very simple cases. We propose a more efficient approach that combines discrete (design) and continuous (path) optimization in a single stage. It builds on an extension of a sampling-based algorithm, which simultaneously explores the configuration-space costmap of all possible designs aiming to find the best path-design pair. The algorithm filters out unsuitable designs during the path search, which breaks down the combinatorial explosion. Illustrative results are presented for relatively simple (academic) examples. While our work is currently motivated by problems in computational biology, several applications in robotics can also be envisioned.