This letter considers the sparse array configuration optimization problem for distributed inverse synthetic aperture radar (ISAR) imaging of long-range targets, aiming to suppress the strong grating lobes caused by sparse sampling. Existing configuration optimization methods for a single array face two challenges: 1) The distributed ISAR model requires considering both the array configuration and ISAR temporal sampling, and typical models fail to apply; 2) Existing methods typically suppress grating lobes over the entire angular parameter space, which disperses the suppression capability too broadly and yields insufficient performance gains. To overcome these challenges, we propose a distributed ISAR configuration optimization method. Particularly, we construct an equivalent distributed array model based on phase equivalence, and incorporate target prior information to constrain the angular parameter space, thereby enhancing the grating lobe suppression capability within the region of interest. Comparative simulation experiments validate that the proposed method improves the peak grating lobe ratio (PGLR) by over 3 dB within the region of interest.