In logistics distribution planning, the distribution plan is often affected by the division of the distribution scope, which determines the distribution cost. To reduce the cost of logistics distribution, decrease the travelling time of distribution and improve the efficiency of logistics distribution, the distribution scope co-evolutionary optimization (DSCEO) model is proposed. Different from the traditional method of distribution scope division, DSCEO not only considers the Euclidean distance, but also adds the limitation on the capacity and distribution scope of each distribution centre, and sets up a two-step matching mechanism for centres and sites to facilitate co-evolutionary optimization. At the same time, the improved boundary ripple spreading algorithm (BRSA) is used as the implementation of DSCEO, which makes the solution model more effective. In this paper, the modelling principle and solution method of DSCEO are described in detail, and 100 random experiments are carried out to verify its universality and effectiveness. Experimental results show that, under different road network conditions, compared with traditional k-means clustering algorithm, the travelling time of distribution vehicles decreased by 13.0% ~ 55.7%, and the distribution cost decreased by 11.4% ~ 23.4%, which demonstrates the DSCEO model proposed in this paper has strong optimization performance.

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