<p>Monte Carlo simulation (MCS) is frequently used as a benchmark for testing other probabilistic evaluation methods in geotechnical engineering, owing to its simple implementation and high accuracy. However, the practical application of MCS is often limited by the heavy computational burden associated with deterministic evaluations of slope systems. This study introduces an efficient method based on Monte Carlo simulation for probabilistic stability evaluation of soil slopes. The proposed method leverages the intrinsic physical information of a slope stability model to improve computational efficiency. The method features two distinct aspects: (1) it uses the strength reduction sampling method to identify critical slope samples in the limit state, and (2) it considers the direct positive correlation between the safety factor of slope and shear strength parameters to determine the safety or failure of a slope sample based on the obtained critical samples, without extra calculations of safety factor. Several typical slope examples are employed to demonstrate the accuracy and efficiency of the new method. The results show that the method maintains the same estimation accuracy of failure probability as the direct MCS, while significantly reducing the number of deterministic slope model evaluations. Consequently, the presented approach offers a simple and promising tool for advancing geotechnical reliability analysis and reliability-based design.</p>