Autonomous Driving Systems (ADS), as the core of next-generation intelligent transportation, require rigorous safety verification to achieve large-scale application. However, traditional real-vehicle testing faces bottlenecks such as high costs and limited scenario coverage. For this reason, simulation testing has become the mainstream solution for ADS testing due to its advantages of low cost, high efficiency, and comprehensive scenario coverage. This paper systematically organizes the theoretical methodologies and related tools for simulation-based ADS testing. It first elaborates on the two core architectures of modular and end-to-end architectures, as well as the differences between module-level and system-level testing; then details the three simulation testing levels, and compares and analyzes the characteristics of mainstream simulation platforms; further explores scenario-based testing methods, including search-based testing for identifying edge risks, metamorphic testing for solving the problem of verification without ground truth, and formal methods for ensuring system compliance; finally constructs a multi-dimensional safety evaluation index system covering collision and conflict, trajectory and lane-keeping, comfort and smoothness, and scenario coverage. This paper aims to provide a structured knowledge system for ADS simulation testing in the current field and to offer research directions for subsequent technological iteration and commercial implementation.