Computer designers now have more transistors and architectural alternatives than at any time. Computer-aided design tools automate much of the physical design process. However, few tools have been developed to help the computer architect specify near-optimal microarchitectural configurations in the early design stages. Such tools are needed to systematically guide the early design specifications subject to multiple objectives such as cost, performance, and power consumption. This paper illustrates an objective-driven microarchitectural design methodology that couples the specification design phase with an optimization technique. The design of a memory hierarchy with multiple performance objectives is used as a case study. This is a directed search problem with a high dimensionality. We show that the genetic algorithm, a global optimization technique based on the metaphor of natural selection and survival of the fittest, is an ideal candidate for such an objective-driven search in a high dimensional space. The paper concludes that search techniques such as genetic algorithms are necessary to systematically and efficiently drive architectural optimizations for multiple objectives such as dynamic power, and performance in the early, high-impact stages of the design process.