In order to realize the high efficiency distribution of urban cold chain logistics, this paper is aimed at the study of urban cold chain logistics path optimization problems considering carbon constraints. First, based on the actual situation of urban traffic congestion, regional pollution limitations, product time-varying quality, customer distribution time requirements, and vehicle load limitations coexisting, the comprehensive consideration of customer demand, service time, driving speed, and load capacity on the impact of fuel consumption, and the objective of constructing a vehicle path planning model with the goal of minimizing the sum of the fixed cost, fuel cost, penalty cost, and cargo damage cost. Secondly, a hybrid genetic algorithm is designed to solve the problem, which adopts dynamic crossover and mutation operators to accelerate the speed of population optimization and introduces removal and insertion operators to improve the local search ability of the algorithm. Finally, the feasibility of this paper’s model and the superiority of this paper’s algorithm are proved through the solving of examples and cases and the solving of multi-group comparison experiments. The research results can provide a theoretical basis for the optimization of distribution schemes for urban road cold chain logistics enterprises.