This work presents a new methodology to evaluate reliability índices in composite generation and transmission systems. Through this new approach is possible to achieve an accurate estimate of the reliability worth of power systems. The modeling flexibility and simulation efficiency are basic features of the proposed approach. Moreover, it is introduced a new reliability index called LOLC (loss of load cost), which provides a complete information about the economic impact of customer supply interruptions. Bearing in mind this objective was developed a new developed a new simulation model, named pseudo-sequential, based on the nonsequencial sampling to locate the failed states and on the chronological simulation of only the sub- sequences associated with these failed states. In case studies with the IEEE Modified Reliability Test System (MRST) and the Brazilian southeastern system, the pseudo- sequential simulation has shown about twenty-five to thirty times faster than the convential sequential Monte Carlo simulation, based on the relative uncertainly of LOLC estimate. In order to improve the efficacy of the new approach, it is examined the application of one variance reduction technique named importance sampling. The results prove the ability of increasing the speedup through trivial sampling strategies. Another alternative to asses the reliability worth is markovian pseudo-sequential simulation. In this approach, the system is modeled by a homogeneus. Markov process and the state transition method is applied to establish the supply interruptions. The flexibility and accuracy of the original pseudo-sequential method are partially retained. Finally, it is used a customized variance reduction scheme to test pseudo-sequential simulation in equivalent systems.