This paper considers a new method that enables a genetic algorithm (GA) to identify and maintain multiple optima of a multimodal function, by creating subpopulations within the niches defined by the multiple optima, thus warranting a good'diversity'. The algorithm is based on a splitting of the traditional GA into a sequence of two processes. Since the GA behavior is determined by the exploration/exploitation balance, during the first step (Exploration), the multipopulation GA coupled with a speciation method detects the potential niches by classifyin'similar' individuals in the same population. Once the niches are detected, the algorithm achieves an intensification (Exploitation), by allocating a separate portion of the search space to each population. These two steps are alternately performed at a given frequency. Empirical results obtained with F6 Schaffer's function are then presented to show the reliability of the algorithm.