In this paper, we focus on MPI collective algorithm selection process and explore the applicability of the quadtree encoding method to this problem. During the algorithm selection process, a particular MPI collective algorithm is selected based on the collective operation parameters. We construct quadtrees with different properties from the measured algorithm performance data and analyze the quality and performance of decision functions generated from these trees. The experimental data indicates that in some cases, the decision function based on quadtree structure with a mean depth of 3 can incur as little as a 5 percent performance penalty on average. The exact, experimentally measured, decision function for all tested collectives could be fully represented using quadtrees with a maximum of 6 levels. These results indicate that quadtrees may be a feasible choice for both processing of the performance data and automatic decision function generation.