With the rise of emerging technologies such as big data and artificial intelligence, the shift schedule of vehicle automatic transmissions is also gradually evolving towards intelligence. However, there has been no systematic research on evaluation methods for intelligent shift schedules, making it difficult to assess the degree of intelligence in shift schedules and provide guidance for their formulation. To address these issues, this paper proposes an evaluation model for the intelligence level of vehicle shift schedules based on the Analytic Hierarchy Process (AHP), Coefficient of Variation (CV) method, and Fuzzy Comprehensive Evaluation (FCE) method. Firstly, driving cycles are classified based on natural driving data, and fuel economy, smoothness, power performance, and auxiliary braking are selected as the primary indicators of the evaluation model. Fuel consumption, shift frequency, reserve power, and drag torque are chosen as the secondary indicators corresponding to the primary indicators under different driving cycles. Secondly, the AHP, CV method, and fuzzy inference are utilized to calculate the subjective and objective weights of the primary and secondary evaluation indicators, ultimately leading to the establishment of the evaluation model for the intelligence level of vehicle shift schedules. Two different shift schedules are selected for validation. The results indicate that the proposed evaluation model can effectively evaluate the intelligence level of vehicle shift schedules, providing a reference for the optimization and adjustment of vehicle shift schedules, thereby enhancing overall vehicle performance and intelligence.