Optimization of parameters or “systems” in general plays an ever-increasing role in mathematics, economics, engineering, and the life sciences. As a result, a wide variety of both traditional mathematical and nontraditional algorithmic approaches have been introduced to solve challenging and practically relevant optimization problems. Evolutionary optimization methods—in the form of genetic algorithms, genetic programming, and evolution strategies—represent nontraditional optimization algorithms that draw inspiration from the processes of natural evolution. Particle swarm optimization is another set of more recently developed algorithmic optimizers inspired by social behaviors of organisms such as birds [1] and social insects. These new evolutionary approaches in optimization are now entering the stage and are becoming very successful tools for solving real-world optimization problems [2]. We present Visplore and Evolvica as a toolkit to investigate, explore, and visualize evolutionary and swarm-based optimization techniques. A webMathematica interface is also available.