Structure characterization of intrinsically disordered proteins (IDPs) remains a key obstacle in understanding their functional mechanisms. Due to the highly dynamic feature of IDPs, structure ensembles instead of static unique structures are often derived from experimental data. Several state-of-the-art computational methods have been developed to select an optimal ensemble from a pregenerated structure pool, but they suffer from low efficiency for large IDPs. Here we present a matching pursuit genetic algorithm (MPGA) for structure ensemble determination, which takes advantages from both matching pursuit (MP) to reduce the search space and genetic algorithm (GA) to reduce the restriction on constraint types. The MPGA method is validated using a reference ensemble with predefined structures. In comparison with the conventional GA, MPGA takes much less computational time for large IDPs. The utility of the method is demonstrated by application to structure ensemble determination of a mechanosensing protein domain with 306 amino acids. The structure ensemble determined reveals that the N-terminal region 1-240 is more compact than the C-terminal region 240-306. The unique structural feature explains why only a small portion of YXXP tyrosine residues can be phosphorylated easily by kinases in the absence of extension force and why the phosphorylation is force-dependent.