As a new granular computing model, partition order product space can describe and solve problems from multiple views and levels. Its problem solving space is a lattice structure composed of multiple problem solving levels, and each problem solving level is composed of multiple one-level views. How to choose the problem solving level in the partition order product space is an NP-hard problem. Therefore, this study proposes a two-stage adaptive genetic algorithm (TSAGA) to find the problem solving level. First, real encoding is used to encode the problem solving level, and then the fitness function is defined according to the classification accuracy and granularity of the problem solving level. The first stage of the algorithm is based on a classical genetic algorithm, and some excellent problem solving levels are pre-selected as part of the initial population of the second stage, so as to optimize the problem solving space. In the second stage of the algorithm, an adaptive selection operator, adaptive crossover operator, and adaptive large-mutation operator are proposed, which can dynamically change with the number of iterations of the current population evolution, so as to further select the problem solving level in the optimized problem solving space. Experimental results demonstrate the effectiveness of the proposed method.