The work aims to reduce the order processing time of the AutoStore system under the common storage strategy and solve the storage problem encountered by companies with a wide range of goods but limited storage area. A storage method of cargo consolidation was proposed, and a hybrid optimization algorithm of association rule mining algorithm and chaotic seed optimization algorithm was designed for solution. Then, the analysis was carried out from the three aspects of robot turning operation, bin distribution rules, and system layout. Combined with AutoStore operation rules, a mathematical model with the shortest walking distance of robot picking as the objective function was established to output the heat of the goods through the Apriori algorithm, and input the chaotic seed algorithm to get the storage allocation result, thus realizing the linkage decision-making. The chaotic sequence was added in the iteration of the basic seed algorithm to help the seed avoid local minima. The case analysis showed that the cargo consolidation method could significantly reduce the order processing time of the AutoStore system under common storage, and narrow the gap with the specified storage strategy order processing time. Compared with the particle swarm optimization algorithm and the basic seed optimization algorithm, the chaotic seed optimization algorithm had higher optimization efficiency and better stability. This research provides a more suitable cargo storage strategy for companies with a wide range of business categories but limited storage area, which has strong operability and practical significance.