We describe updates to the redMaPPer algorithm, a photometric red-sequence cluster finder specifically designed for large photometric surveys. The updated algorithm is applied to 150deg^2^ of Science Verification (SV) data from the Dark Energy Survey (DES), and to the Sloan Digital Sky Survey (SDSS) DR8 photometric data set. The DES SV catalog is locally volume limited and contains 786 clusters with richness {lambda}>20 (roughly equivalent to M_500c_>~10^14^h_70_^-1^M_{sun}_) and 0.2<z<0.9. The DR8 catalog consists of 26311 clusters with 0.08<z<0.6, with a sharply increasing richness threshold as a function of redshift for z>~0.35. The photometric redshift performance of both catalogs is shown to be excellent, with photometric redshift uncertainties controlled at the {sigma}_z_/(1+z)~0.01 level for z<~0.7 , rising to ~0.02 at z~0.9 in DES SV. We make use of Chandra and XMM X-ray and South Pole Telescope Sunyaev-Zeldovich data to show that the centering performance and mass-richness scatter are consistent with expectations based on prior runs of redMaPPer on SDSS data. We also show how the redMaPPer photo-z and richness estimates are relatively insensitive to imperfect star/galaxy separation and small-scale star masks.