In B2B(Business to Business) distribution, the customer usually has high requirements on the regularity of delivery time to accommodate its production and operation activities. This study investigates the vehicle routing problem considering time window consistency, constructs its mixed integer linear programming model, and develops an adaptive large neighborhood search algorithm to solve it. To address the consistency coupling relationship between daily routes in the time dimension, this study proposes an optimization strategy that combines distance-first and time-window-first techniques, where time window assignment and consistency check models are embedded, and the interdependent operators are designed to optimize the routes on different days collaboratively. The proposed model and algorithm are tested based on the existing dataset, self-built dataset, and a large-scale real-world case. The results show that the proposed algorithm can obtain high-quality solutions efficiently, and the proposed time-window-first technique and the corresponding interdependent operators can significantly improve the performance of the algorithm. Finally, the additional transportation cost of achieving time window consistency is quantified and analyzed, and the variation of consistency costs under different customer sizes and time window lengths is revealed.