Traditional methods to evaluate the ventricular mechanics need intraventricular pressure and volume recordings for multiple variably loaded beats. To do this, a complex and invasive procedure must be applied, that may decrease the clinical use. To overcome this limitation, a method to estimate the ventricular mechanics beat-by-beat is presented, modeling the ventricular pressure-volume relationship with a time-varying elastance function. The ability of the genetic algorithms (GAs) as identification technique is exploited. Applying GAs on surrogated data simulating variably loading conditions, the parameters of the time-varying elastance function, considered a measure of the contractility of the myocardial fibers are identified. These single-beat estimates are highly correlated with the end systolic pressure-volume relationship slope obtained by conventional multiple-beat analysis. The main advantage in using GAs for single beat analysis may lie, in the perspective of an use for in vivo investigations, both in their stochastic nature, and in the guaranteed better performance with respect to other search techniques on problems involving noisy signals. Future studies will approach the reduction in GAs computational costs, for a real time in vivo application.