This chapter considers a class of optimization techniques that were developed to imitate processes found in nature. Nature is a wonderful source of inspiration for global optimization because so many aspects of natural phenomenon can be mimicked and employed for solving challenging design problems, from the very process of evolution to the coordinated search behavior of various swarming organisms. Nature inspired search algorithms have played an important role in electromagnetic design, as they have proven to be very robust at solving complex problems with many design parameters. Also, as the field of metamaterials has developed, optimization has become an important tool in the quest to overcome performance limitations such as high loss and narrow bandwidth, which have limited the widespread use of metamaterials in practical device applications. In the first part of this chapter, three prominent nature inspired optimization algorithms are described in detail, including the genetic algorithm (GA), particle swarm optimization (PSO), and the covariance matrix adaptation evolutionary strategy (CMA-ES). Following this, several examples of metamaterial surfaces are presented that have each been optimized by one of the three nature inspired techniques. Finally, two homogenization techniques that can be employed to invert scattering parameters for a slab of metamaterial to obtain isotropic or anisotropic effective medium parameters are examined and used in conjunction with a GA to overcome previous limitations in terms of loss and angular stability in metamaterials.