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In designing complex systems, systems engineers strive to turn an existing situation into a situation that is most preferred. A rational decision maker would choose the alternative that maximizes the expected utility of the existing situation, but there are significant computational challenges to this approach. To overcome these challenges, most decision makers revert to heuristics. In this paper, we propose a conceptual framework for heuristics in design. A preliminary empirical study of designers for a robotics design problem was conducted to observe how participants apply heuristics. Participants having at least 2 years of experience designing robots were recruited to partake in a robotics design session in which participant were given 45 minutes to work on a design problem. A preliminary heuristics extraction method was developed, and the identified heuristics were studied to find trends within the overall set. These trends were the basis of a taxonomy of heuristics consisting of three initial classification methods: design phase, field of study, and action intent. The heuristics and classifications are presented, along with the challenges from extracting heuristics and recommendations for future work to further research design heuristics and to improve the method for extraction.