This paper shows that, in case of high sensitivity to data econometric modeling, using evolutionary algorithms (Genetic Algorithms - GA and Simulated Annealing - SA) is better than using classical gradient techniques. The evaluation of the algorithms involved was performed on a short form of an economic macromodel. The optimization task is the model’s solution, as function of the initial values (in the first stage) and of the objective functions (in the second stage). We proved that a priori information help “elitist” algorithms (like SA) to obtain best results; on the other hand, when one has equal believe concerning the choice among different objective functions, GA gives a straight answer. Analyzing the average related bias of the model’s solution proved the efficiency of the stochastic optimization methods presented.