This paper addresses the simultaneous design and path-planning problem, in which features associated to the bodies of a mobile system must 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 straightforward only 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) robotic examples, showing that even in these simple cases, the computational cost can be reduced by two orders of magnitude with respect to the naïve approach. A preliminary application to challenging problems in computational biology related to protein design is also discussed.