Stochastic global optimization algorithms have shown promise in providing reliable and efficient solutions for phase stability and phase equilibrium problems in reactive and nonreactive systems. A special class of stochastic methods is Swarm Intelligence, in which search agents are allowed to interact with each other and with their environment and benefit from their peers in their collective pursuit for the global minimum, resulting in an intelligent behavior unknown to the individual agents. Of special interest are swarm intelligence methods with less tunable algorithm parameters, which allow for easy and user-friendly implementation. In particular, this study introduces the Charged System Search, a novel swarm intelligence method, as a global optimization tool to the Chemical Engineering literature via implementing it, for the first time, in solving phase stability and equilibrium problems. Two Charged System Search variants have been employed, namely, the Magnetic Charged System Search and the hybrid version with Particle Swarm Optimization. This hybrid method is coupled with chaotic maps to overcome the local optimum entrapment and to aid its exploration capability. Results indicate that these two variants generally outperformed the Charged System Search, especially the hybrid chaotic algorithm. Results of this study were also compared to those reported for other swarm intelligence methods applied in phase equilibrium calculations. In summary, this study introduces novel swarm intelligence methods for performing phase stability and equilibrium calculations in both reactive and nonreactive systems.