The logistics and distribution problems of electric energy measuring instruments include demand prediction, distribution center location, customer distribution, etc. An optimization strategy for electric energy measuring instru-ment distribution network is proposed based on an improved immune genetic algorithm and Holt-Winters model in this paper. Firstly, the Holt-winters model is applied to predict the monthly demand in the next year. The golden section method can be utilized to search for the optimal smoothing coefficient to improve prediction accuracy. Then, the Gauss projection method is employed to transform the longitude and latitude of customers into Cartesian coordinates, and the objective function of minimum transportation costs is constructed. The antibody concentration of the immune genetic algorithm is improved based on the similarity and vector distance between populations. Finally, the power meter dis-tribution problem of metering center-distribution center-customer in a provincial power grid is included as an example. The improved immune genetic algorithm solves the objective function and selects the optimal distribution center loca-tion and customer distribution scheme. It is shown that the improved immune genetic algorithm has higher conver-gence efficiency and the ability to avoid local convergence. The proposed optimization strategy of the electric energy measuring instrument distribution network has a certain reference value to reduce the distribution costs.