Genetic algorithms are very efficient in the problems of exploration and seem to be able to find the single optimal solution in a huge space of possible solutions. However, they are ineffective when it comes to finding the exact value of the optimum in this space. This is precisely what the exact optimization algorithms perform best. It is therefore normal to think of combining exact and genetic algorithm to find the exact value of the optimum. Mc-RULEGEN is a new system to extract symbolic rules from a trained neural network, based on two approaches genetic and exact. MC-RULEGEN consists of three major components: A multi-layer perceptron neural component, a genetic component, a simplification rules component based on Quine McCluskey algorithm. Our method is tested on breast cancer and iris databases, the computational results have shown that the performances of the rules extracted by MC-RULEGEN are very high.