In this work, a new optimization method inspired on the Thermodynamic Equilibrium is described to address nonlinear problems in continuous domains. In our proposal, each decision variable is treated as the most volatile chemical component of a saturated binary liquid mixture at a determined pressure and temperature. The optimization procedure is started with an initial solution randomly generated. The search is done by changing the equilibrium state of each mixture. The search is carried out by accepting worse solutions to avoid being left trapped in local optimums. The search includes the random change of the mixtures. The algorithm was tested by using known mathematical functions as benchmark functions showing competitive results in comparison with other metaheuristics.