The design of antennas, frequency selective surfaces, arrays, and other electromagnetic devices often requires tuning of many geometric and material parameters to obtain a desired set of characteristics. Typically, a genetic algorithm, particle swarm, or other standard optimization technique is used to obtain a suitable set of parameters that fulfill the design criteria. Although these methods have been shown to be relatively robust and reliable, they often require long optimization times or function evaluations, usually in the form of many simulations, to find an acceptable solution. Many new and improved evolutionary strategies have arisen since the inception of the genetic algorithm  and particle swarm techniques. The covariance matrix adaptation evolutionary strategy (CMA-ES) is a recently designed algorithm that has generated a great deal of interest in the evolutionary computation community. In addition to being a fast strategy, CMA also requires very few algorithm parameters due to its adaptive nature, eliminating issues with user setting selections . In this paper, this new algorithm is applied to an antenna optimization problem and comparisons are made to a particle swarm technique , demonstrating the appealing properties of CMA that make it ideally suited for electromagnetics design.