As autonomous driving applications are rapidly popularized, their safety has become the common focus of both academia and industry. Autonomous driving system (ADS) testing is an effective means for solving this problem. Currently, the mainstream testing method is the scenario-based simulation test, which evaluates the decision of ADS to be measured by simulating various elements of driving scenarios, such as roads and pedestrians. However, existing methods mainly focus on the construction and dynamic generation of critical driving scenarios, neglecting the influence of configuration changes of the vehicle itself, such as its weight and torque, on the decision-making of ADS deployed on the vehicle. To address this issue, based on the previous work SAFEVAR, this study proposes SAFEVCS, an efficient search method for safety-critical vehicle configurations. SAFEVCS employs a search algorithm to explore the vehicle configuration setting (VCS) that exposes safety vulnerabilities of ADS. Furthermore, to improve the diversity of the search results, SAFEVCS introduces fuzzing to optimize the conditions and constraints of crossover and mutation operators in search algorithms. To improve search efficiency, SAFEVCS further combines the vehicle dynamics knowledge, which achieves the self-adaption of search termination strategy and deduplication strategy. To evaluate the effectiveness and execution efficiency of SAFEVCS, the study takes SAFEVAR as the baseline for comparison and carries out extensive experiments under three driving scenarios. The experimental results show that VCS generated by SAFEVCS can effectively expose the safety vulnerabilities of ADS. In the two weather conditions of sunny and rainy days, under the simulation scenarios of pedestrians crossing the road, the obtained solution set significantly decreased the safety performance of the ADS under test, and under the same experiment environment, the simulation efficiency is increased by approximately 2.5 times.