The drive toward robust systems design, especially with respect to system affordability throughout the system life-cycle, has led to the development of several advanced design methods. While these methods have been extremely successful in satisfying the needs for which they have been developed, they inherently leave a critical area unaddressed. None of them fully considers the effect of requirements on the selection of solution systems. The goal of all the current modern design methodologies is to bring knowledge forward in the design process to the regions where more design freedom is available and design changes cost less. Therefore, it seems reasonable to consider the point in the design process where the greatest restrictions are placed on the final design, the point in which the system requirements are set. Historically, the requirements have been treated as something handed down from above. At best, a negotiation would take place to settle on a specific set of requirements that were acceptable to both the customer and the solution provider. However, in both of these cases, neither the customer nor the solution provider completely understood all of the options that are available in the broader requirements space. If a method were developed that provided the ability to understand the full scope of the requirements space, it would allow for a better comparison of potential solution systems with respect to both the current and potential future requirements. Furthermore, by evaluating the inclusion of current or new technologies, it is possible to identify not only which systems can fulfill a certain set of requirements, but also which technologies will enable the satisfaction of those requirements. The key to a requirements conscious method is to treat requirements differently from the traditional approach. The method proposed herein is referred to as Requirements Controlled Design (RCD). By treating the requirements as a set of variables that control the behavior of the system, instead of variables that only define the response of the system, it is possible to determine a priori what portions of the requirements space that any given system is capable of satisfying. Additionally, it should be possible to identify which systems can satisfy a given set of requirements, which system is the best choice given knowledge of the future requirements trend, and the locations where a small change in one or more requirements poses a significant risk to a design program. This thesis puts forth the theory and methodology to enable RCD, and proposes and evaluates two basic methods, a grid search and an evolutionary, global optimization scheme, for finding the technology boundaries of a system in the requirements hyperspace. Finally a specific method using a Pareto seeking evolutionary algorithm to discover the location of predetermined technology boundaries is described and validated. The algorithm, called Modified Strength Pareto Evolutionary Algorithm (MSPEA), uses advancements in multi-objective, global optimization to enable finding the technology induced boundaries in both the design and requiremensts spaces. Using MSPEA and evaluation of the U.S. Army's Light Helicopter Experimental (LHX) program is also presented. This evaluation demonstrates the capability of both RCD and the MSPEA algorithm and validates the ability of the algorithms to find a historically feasible space.