In this chapter, we present two classes of evolutionary algorithms that are specialized for searching arbitrary real spaces: evolution strategies and differential evolution. Both metaheuristics allow the search for the global optimum of nonlinear, non-convex, possible non-differentiable multimodal functions defined in real n-dimensional space. They are among the most popular methods among engineers and scientists, especially differential evolution, which is simpler conceptually and equally effective in its more advanced versions. We start with the simpler basic algorithms and then gradually add the refinements and improvements that have evolved over the years and that have led to the present-day powerful versions of these two metaheuristics.