As an automatic search tool, mixed integer linear programming (MILP) is widely used to search for differential, linear, integral, and other cryptographic properties of block ciphers. In this study, a new technique of constructing MILP models based on a dynamic selection strategy is proposed, which uses different constraint inequalities to describe the propagation of cryptographic properties under different conditions. Specifically, according to the different Hamming weights of the input division property, this study adopts different methods to construct MILP models of the division property propagation with linear layers. Finally, this technique is applied to search for integral distinguishers of uBlock and Saturnin algorithms. The experimental results show that the proposed technique can obtain an 8-round integral distinguisher with 32 more balance bits than the previous optimal integral distinguisher for the uBlock128 algorithm. In addition, this study gets 9- and 10-round integral distinguishers for uBlock128 and uBlock256 algorithms which are one round longer than the previous optimal integral distinguishers. For the Saturnin256 algorithm, the study finds a 9-round integral distinguisher which is one round longer than the previous optimal integral distinguisher.