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Heuristics are essential for addressing the complexities of engineering design processes. The goodness of heuristics is context-dependent. Appropriately tailored heuristics can enable designers to find good solutions efficiently, and inappropriate heuristics can result in cognitive biases and inferior design outcomes. While there have been several efforts at understanding which heuristics are used by designers, there is a lack of normative understanding about when different heuristics are suitable. Towards addressing this gap, this paper presents a reinforcement learning-based approach to evaluate the goodness of heuristics for three sub-problems commonly faced by designers while carrying out design under resource constraints: (i) learning the mapping between the design space and the performance space, (ii) sequential information acquisition in design, and (iii) decision to stop information acquisition. Using a multi-armed bandit formulation and simulation studies, we learn the heuristics that are suitable for these sub-problems under different resource constraints and problem complexities. The results of our simulation study indicate that the proposed reinforcement learning-based approach can be effective for determining the quality of heuristics for different sub-problems, and how the effectiveness of the heuristics changes as a function of the designer's preference (e.g., performance versus cost), the complexity of the problem, and the resources available.