Ensuring the safety of personnel and providing practical escape routes for potential victims play essential roles in reducing the human vulnerability to site-specific landslides. However, the escape guidelines adopted by government departments and public welfare organizations are vague and unfocused because they ignore the features of individual people and spatial differences among hazard zones. In this paper, a scenario simulation model for quantitatively evaluating the escape behavior and human vulnerability was proposed using a Python platform based on the landslide runout process and hazard zone characteristics. A database that includes detailed information for the hazard zone’s demographic structure and behavioral characteristics was established based on sampling surveys and field investigations. The probability values of scenario input parameters such as the escape route and speed were calculated and quantified by classic probability theory. The registered population over 65 years old accounted for 13.46% of the total, and most residents had no more than a primary school education background. Older adults were inclined to escape a moving landslide by running parallel to the sliding direction, although the public considers this direction to be the most dangerous. The exposure time in the hazard zone is 930 to 980 h per hectare during a corn planting year; the model simulation revealed that the cumulative mortality can be significantly reduced by promoting disaster prevention awareness and improving the advance warning time. This research and the developed quantitative human vulnerability framework provide user-friendly disaster prevention rehearsal guidelines and a reference for human disaster mitigation.