The probabilistic reliability assessment of an electric power system aims to quantify, in terms of risk indices, its inability to fully serve its customers. In prac-tice, deterministic criteria (e.g., N-1) are still the most widely used. In the literature, however, probabilistic analysis is an extensive area of research, which can be di-vided into two evaluation categories: those based on Monte Carlo simulation (MCS) and those based on the state enumeration (SE). Despite being admittedly inferior, the SE technique is the one that most closely resembles the deterministic criteria, and, most likely for this reason, has a wide range of technical publications. How-ever, such works have limitations, because they are either restricted to small sys-tems, or they disregard higher contingency orders, when addressing real systems (medium-large). In any case, there is a strong attachment of the electric sector to reliability techniques that are similar to the practices of operators and planners. This motivated the development of a method based on SE, which is capable of assessing the reliability of generation and transmission systems with performance comparable to that of MCS. In a heterodox way, importance sampling (IS) concepts used in variance reduction techniques (VRT), typically employed by MCS, have served as inspiration to improve SE. Thus, the method proposed in this dissertation is the combination result of an IS-VRT type tool with SE techniques. For the analysis and validation of the proposed method, two test systems commonly used in this research topic are used, one of which is medium-sized and capable of reproducing typical characteristics of real systems.