Slopes are frequently stabilized by reinforcement measures in geological and geotechnical engineering. Effectiveness of reinforcement measures is vital to slope failure risk mitigation, but it is affected by geological and geotechnical uncertainties arising from loads, geological formations and geotechnical properties. In face with these uncertainties, slope reinforcement measures are often monitored at site, and monitoring variables that are sensitive to the safety and reliability of the reinforced slope stability shall be carefully selected during monitoring design, which highly depends on engineering experience and judgments (i.e., prior knowledge). How to exercise the prior knowledge in a quantitative and transparent way in geotechnical monitoring design remains unexplored. This can be accomplished using reliability-based monitoring sensitivity analysis, which requires significant computational costs due to repeated inverse analyses and reliability analyses given different possible values of monitoring variables. This paper develops an efficient reliability-based monitoring sensitivity analysis framework based on BUS (i.e., Bayesian updating with structural reliability methods) with Subset Simulation methods. The proposed approach is illustrated using a real reinforced slope example. Results show that it quantifies reliability sensitivity of different monitoring variables (e.g., displacements at different locations on slope surface) in a cost-effective manner based on prior knowledge. Such reliability sensitivity information facilitates decision-making in selecting sensitive monitoring variables during the monitoring design of reinforced slopes.