Aiming at the problems of premature convergence and easily falling into local optimum in the adaptive large neighborhood search algorithms with single mechanism， a hybrid adaptive large neighborhood search algorithm was proposed to solve Time-Dependent Vehicle Routing Problem （TDVRP） in cold chain logistics. Firstly， the time-varying vehicle speed was described according to the continuous driving time dependent function， the real-time fuel consumption was evaluated by using the comprehensive fuel consumption model， and a routing optimization model with the goal of minimizing the total cost was established. Then， according to the NP （Non-deterministic Polynomial）-hard property and time-dependent characteristics of the problem， a variety of large neighborhood search operators for destroying and repairing solutions were designed， and the destroy-repair large neighborhood search operators were integrated into Artificial Bee Colony （ABC） algorithm to improve the global search ability of the algorithm. Simulation results show that compared with Adaptive Variable Neighborhood Search Elite Ant Colony （AVNS_EAC） algorithm， Adaptive Large Neighborhood Search Elite Ant Colony （ALNS_EAC） algorithm， Adaptive Large Neighborhood Search Elite Genetic （ALNS_EG） algorithm and Adaptive Large Neighborhood Search Simulated Annealing （ALNS_SA） algorithm， the proposed Adaptive Large Neighborhood Search Artificial Bee Colony （ALNS_ABC） algorithm has the optimal fitness values increased by 46.3%， 5.3%， 36.8% and 6% respectively and averagely on multiple test data groups. It can be seen that this algorithm has higher computational performance and stronger stability， and can provide a more reasonable decision-making basis for cold chain logistics enterprises to take into account economic and environmental benefits at the same time.