  We present simulations for the Dark Energy Survey (DES) using a new code
suite (SNANA) that generates realistic supernova light curves accounting for
atmospheric seeing conditions and intrinsic supernova luminosity variations
using MLCS2k2 or SALT2 models. Errors include stat-noise from photo-statistics
and sky noise. We applied SNANA to simulate DES supernova observations and
employed an MLCS-based fitter to obtain the distance modulus for each simulated
light curve. We harnessed the light curves in order to study selection biases
for high-redshift supernovae and to constrain the optimal DES observing
strategy using the Dark Energy Task Force figure of merit.
