In this study, nonlinear dynamics techniques toward detecting cardiac murmurs from phonocardiograms (PCG) are used. With this purpose, a methodology for tuning parameters (reconstruction delay -tau and embedding dimension -m) involved in the reconstruction of a meaningful state space from scalar time series is presented, using genetic algorithms (GA), as well as constructing a meta-algorithm combined with support vector regression to adjust the GA parameters in order to decrease the computational cost. The forecasting capacity is used as cost function of the GA. The PCG records belong to the National University of Colombia, 360 beats were chosen by specialist, 180 normal and 180 with cardiac murmur evidence. The obtained results show that by using the tuned GA an efficient procedure for the consistent determination of tau and m is achieved. Murmur detection by using nonlinear features was obtained with classification accuracy of 96% using a k nearest neighbor classifier in cross-validation with 10 folds.