Proteins of importance to human biology can populate significantly different three-dimensional (3d) structures at equilibrium. By doing so, a protein is able to interface with different molecules in the cell and so modulate its function. A structure-by-structure characterization of a protein's transition between two structures is central to elucidate the role of structural dynamics in regulating molecular interactions, understand the impact of sequence mutations on function, and design molecular therapeutics. Much wet- and dry-laboratory research is devoted to characterizing structural transitions. Computational approaches rely on constructing a full or partial, structured representation of the energy landscape that organizes structures by potential energy. The representation readily yields one or more paths that consist of series of structures connecting start and goal structures of interest. In this paper, we propose instead to cast the problem of computing transition paths as a multiobjective optimization one. We identify two desired characteristics of computed paths, energetic cost and structural resolution, and propose a novel evolutionary algorithm (EA) to compute low-cost and highresolution paths. The EA evolves paths representing a specific structural excursion without a priori constructing the energy landscape. Preliminary applications suggest the EA is effective while operating under a reasonable computational budget.