Aiming at the common problems in the collection and transportation of urban food waste, such as low loading rate or overloading, high vehicle exhaust emissions, strong subjectivity in route planning, high comprehensive costs, and low merchant satisfaction, according to the distribution, collection and transportation characteristics of urban food waste, a model of the dynamic vehicle routing problem with time windows based on traffic flow was established, and an improved genetic algorithm was used to solve it. The static optimization results showed that the strategy of “minimum collection and transportation cost+time window” was identified as the best static optimization strategy. Compared with the scenario without a hard time window, when one more vehicle was used, unit average collection and transportation cost, unit average carbon emission, and unit average fuel consumption were reduced by 8.16%, 12.12%, and 10.48%, respectively. The dynamic optimization results showed that the strategy of “minimum collection and transportation cost+time window+time-discrete” was the best dynamic optimization strategy. In this strategy, compared with the best static optimization strategy, the total cost was reduced by 15.23%, the fuel consumption and carbon emissions were reduced by 24.97%, and unit average collection and transportation cost, unit average carbon emission, and unit average fuel consumption were decreased by 25.85%, 39.39% and 36.36%, respectively. In addition, after simulating the installation of intelligent garbage bins to obtain the real-time amount of food waste, it was verified that the addition of this equipment had a further optimization effect on the proposed model. Finally, an environmental impact assessment was carried out for the actual operation and the six optimization strategies.