The optimization of intelligent warehousing is generally divided into shelf optimization and path optimization. Shelf optimization considers the position of goods and shelves, and optimizes the placement of goods. Path optimization mainly seeks the optimal path planning for automatic guided vehicles. At present, most of the studies focus on these two scenarios independently. In the actual warehousing application, the problem can only be solved by linear superposition, which makes the solution easy to fall into the local optimum. Based on the coupling analysis of the relationship between various sections in the intelligent warehousing process, this study proposes a mathematical model of cooperative optimization of shelf and position, which combines shelf optimization and path planning as a whole. In addition, a cooperative optimization framework, including a product similarity solving algorithm and an improved path planning algorithm, is proposed. Based on the above two algorithms, an improved genetic algorithm is proposed for the cooperative optimization of shelf and path. The experimental results verify the effectiveness and stability of the intelligent warehousing cooperative optimization algorithm proposed in this study. By using this algorithm, it can improve the shipping efficiency of storage and reduce transportation costs..