Phenomena analyzed in the field of engineering with frequency analyses are usually extreme events such as floods and droughts. Such events frequently have as origin the simultaneous occurrence of two phenomena, and therefore are analyzed with two simultaneous probability distributions. Such is the case of the climatological and hydrometric series in Mexico which, due to its exposure to phenomena such as hurricanes, are analyzed with two-population probability distributions. The good use of these distributions depends on the correct estimation of their parameters. The application of a harmonic search meta-heuristic algorithm for the estimation of the parameters that optimize the univariate Gumbel Mixed function is presented. Annual maximum hydrometric data are used to compare the best-fit of the univariate distribution with traditional methodologies, such as maximum likelihood and the Rosenbrock algorithm. The results show that there is a decrease in the root mean square error and a fast convergence when a harmonic search algorithm is used. With the decrease of the error of fit, estimation of the design flows values is improved. The pseudocode of the algorithm for the implementation of the proposed methodology is presented.