Metaheuristic algorithms are well-known optimization tools which have been used in many applications including those in data mining. In particular, these algorithms are well suited for solving nonconvex clustering problems since they are able to find global solutions, whereas traditional clustering algorithms such as k-means can only guarantee convergence to a local solution. Metaheuristic algorithms applied to solve clustering problems include the tabu search and the simulated annealing algorithms as well as evolutionary algorithms like genetic algorithm, artificial bee colony optimization, particle swarm optimization, and ant colony optimization algorithms. The description of these algorithms and their flowcharts are given in this chapter.