Software systems are likely to face what is called design problems. A design problem is the result of bad decisions that can aect some important quality attributes of the software system such as maintainability, performance and the like. Given the typical lack of design documentation, developers have to rely on implementation-level symptoms to identify and remove design problems. An implementation-level symptom usually manifests as a code smell, a micro-structure in the program possibly indicating the presence of (or part of) a design problem. Large programs have hundreds or thousands of program elements (packages, classes, interfaces, and the like) in which a significant proportion is aected by smells. However, many of these smells may bear no relationship with design problems, i.e. they are not design-relevant smells. Then, it becomes hard and time-consuming to prioritize smelly program elements being suspects of having a design problem. Unfortunately, the literature fails to provide developers with heuristics to support the prioritization of these suspicious program elements. In this context, this dissertation reports two studies aimed at assisting in the elaboration of such prioritization heuristics. The goal of these heuristics is to locate a short (high priority) list of smelly program elements, which are suspects of having design-relevant smells. Our first study consists of a qualitative analysis on recurring criteria used by developers, in practice, to prioritize elements suspicious of having design problems. Based on these criteria, we derived a preliminary suite of prioritization heuristics. Our second study focused on the evaluation of the proposed heuristics. As a result, we found that two out of nine heuristics reached the best results in precision. The best heuristics are based on two criteria: smell diversity and smell granularity. Our findings suggest that we were able to derive a first promising approach to support developers in prioritizing elements with design-relevant smells.