In order to solve the problem of urban traffic congestion and long peak time, this paper combines the characteristics of dynamic traffic congestion in Dalian and the actual scene of logistics distribution in the same city, taking into account the constraints of transportation cost, equipment loss cost, intelligent construction cost, distribution timeliness, and customer satisfaction, and taking the minimization of the total cost of global distribution as the core goal, a dynamic logistics distribution path optimization model based on traffic big data is constructed. The model is fully integrated into the quantitative indicators of road congestion in various periods and regions of Dalian, and solves the defects that the traditional static path model cannot adapt to tidal traffic and dynamic congestion, and adapts the improved genetic algorithm to solve the problem.