Phase equilibrium calculations require global minimization of free energy, and phase stability analysis too often involves global minimization of tangent plane distance function (TPDF). In this study, two stochastic global optimization techniques, namely, genetic algorithm (GA) and simulated annealing (SA) are evaluated and compared for phase equilibrium and stability problems. Typical examples and different thermodynamic models are considered. The results show that GA is generally more efficient and reliable than SA for phase equilibrium calculations. Both GA and SA exhibited poor reliability for locating the global minimum of free energy function for some complex phase equilibrium systems. For these problems, a hybrid GA incorporating SA for individual learning, is proposed and its improved capability is shown. The results on phase stability problems show that GA is able to locate the global minimum of TPDF with 100% reliability in all the examples tried. It is also found to be very efficient compared to other global techniques reported in the literature.