Monte Carlo simulation (MCS) is recognized as an efficient tool in the reliability analysis of large composite generation and transmission systems. In practice, however, planners are more interested in evaluating only their operating regions, especially areas and buses where there is a greater concentration of consumers. In this case, failure is often a rare event, which substantially increases the computational cost of the MCS. To deal with this problem, the use of variance reduction techniques has been proposed in numerous works, which present good results, but still find some difficulty in modeling problems involving the physical restrictions of the transmission network. In this work, a new method for nonsequential MCS, aided by importance sampling via the cross-entropy method, is proposed through an adherent approach to the composite reliability problem, mainly focusing on specific areas and buses of the system. The method is evaluated using a modified version of the IEEE Reliability Test System – 1996, which presents similar rarity levels of compound failures as in real systems.