Crowd behavior is the collective act and gathering of a group of individuals to achieve a shared purpose. Swarm intelligence-based optimization algorithms are usually used to solve complex problems for crowd behavior. Crowd simulations are often used for the analyses that require precision in different domains such as complex structural analysis, image recognition, creating nature-inspired non-player character movements in video games, and more. In this study, a generic crowd simulation framework that can be used to simulate already-available crowd simulation algorithms and design new ones was developed. The test environment layout was generated with the use of a generate-and-test algorithm combined with the crowd simulation algorithms to make sure that the generated content is meeting the requirements of a crowd simulation environment. Within the framework, three different crowd simulation algorithms —firefly algorithm, particle swarm optimization, and artificial bee colony— are generated and also implemented as puzzle-like video games. The results show that all fireflies achieved to gather at the global minimum of the generated layout faster and in a more precise way than the artificial bee colony algorithm and particle swarm optimization algorithm. The developed framework enables a generic and parametric testbed to design and compare different algorithms and to generate video games.