Abstract: Monte Carlo simulation has become an important tool for estimating the reliability and availability of dynamic system, since conventional numerical methods are no longer efficient when the size of the system to solve is large. However. evaluating by a simulation the probability of occurrence of very rare events means playing a very large number of histories of the system, which leads to unacceptable computing time. Highly efficient Monte Carlo should he worked out. In this paper, based on the integral equation describing state transitions of Markov dynamic system, a uniform Monte Carlo for estimating unavailability is presented. Using free-flight estimator, direct statistical estimation Monte Carlo is achieved. Using both free flight estimator and biased probability space of sampling, weighted statistical estimation Monte Carlo is also achieved. Five Monte Carlo schemes, including crude simulation, analog simulation, statistical estimation based on crude and analog simulation, and weighted statistical estimation, are used for calculating the unavailability of a repairable Con/3/30:F system. Their efficiencies are compared with each other. The results show the weighted statistical estimation Monte Carlo has the smallest variance and the highest efficiency in very rare events simulation.