In order to select high-value endorsers in social networks to achieve the best possible mobile coupon distribution effect. Firstly, the endorser's social communication ability is modeled according to the number of fans and active status. At the same time, the perference degree of mobile coupon types and the forwarding rate of mobile coupons are used to model the individual's sharing willingness of the endorser. Secondly, the concept of endorser value is proposed from the social communication ability and the individual sharing willingness, and the endorser value ranking algorithm is designed. Then, on the basis of considering the value of the endorser, aiming at the multi-objective optimization problem of maximizing the profit of the firm and the profit of the endorsers, a mobile coupon distribution model is established, and the high face-value non-dominated sorting genetic algorithm (HFNSGA) based on the genetic algorithm is designed, which can realize the distribution of mobile coupons based on the endorser value in social networks. Finally, experiments are conducted on the real user data set on the GitHub, the results show that the EVRank algorithm is superior to other related algorithms in accuracy and matching rate. At the same time, the example analysis shows that the HFNSGA algorithm can not only effectively solve the high-dimensional multi-objective optimization problem, but also its solution set has good distribution and uniformity, which can effectively guide enterprise to make mobile coupon distribution decision.