This paper presents a new approach for the task of feedforward type neural network training process based on a multi-criterion efficiency measurement. Here we propose a novel hybrid neuro-genetic algorithm that tries to optimize a three dimension criterion vector composed by speed, accuracy, and percentage of convergence, which measures the overall stability of the training algorithm to converge to good minimal. The proposed approach takes the speed advantage of the conventional algorithms as well as the accuracy and percentage of convergence advantages of the genetic algorithms. The empirical results obtained up to now shows the strength and potentiality of the method.