We propose to identify a sample of galaxies in the Dark Energy Survey (DES) photometry that corresponds to a cleanly identifiable subset of the SDSS-BOSS CMASS sample. The CMASS sample is originally designed from the Sloan Digital Sky Survey and provides the most powerful redshift-space galaxy clustering measurements to date. A joint analysis of redshift-space distortions (such as those probed by CMASS from SDSS) and galaxy-galaxy lensing measurement for an equivalent sample from DES can provide a powerful cosmological constraints. Unfortunately, the DES and SDSS-BOSS footprints suffer minimal overlap, primarily on the celestial equator near the SDSS Stripe 82 region. We have built a robust Bayesian model to select CMASS galaxies in the DES footprint specifically to address this lack of overlap. We show that the newly defined DES-CMASS (DMASS) sample selected by this model has a fairly good match with the CMASS sample through various validations.