Advances in ocean modeling (Barron et al., 2006) have improved such that ocean forecasts and even ensembles (e.g., Coelho et al., 2009) representing ocean uncertainty are becoming more widely available. This facilitates nowcasts (current time ocean fields / analyses) and forecasts (predicted ocean fields) of acoustic propagation conditions in the ocean which can greatly improve the planning of acoustic experiments. Modeling of acoustic transmission loss (TL) provides information about how the environment impacts acoustic performance for various systems and system configurations of interest. It is, however, very time consuming to compute acoustic propagation to and from many potential source and receiver locations for multiple locations on an area-wide grid for multiple analysis / forecast times, ensembles and scenarios of interest. Currently, to make such wide area predictions, an area is gridded and acoustic predictions for multiple directions (or radials) at each grid point for a single time period or ensemble, are computed to estimate performance on the grid. This grid generally does not consider the environment and can neglect important environmental acoustic features or can overcompute in areas of environmental acoustic isotropy. This effort develops two methods to pre-examine the area and time frame in terms of the environmental acoustics in order to prescribe an environmentally optimized computational grid that takes advantage of environmental-acoustic similarities and differences to characterize an area, time frame and ensemble with fewer acoustic model predictions and thus less computation time. Such improvement allows for a more thorough characterization of the time frame and area of interest. The first method is based on critical factors in the environment that typically indicate acoustic response, and the second method is based on a more robust full waveguide mode-based description of the environment. Results are shown for the critical factors method and show that this proves to be a viable solution for most cases studied. Limitations are at areas of high loss, which may not be of concern for exercise planning. The mode-based method is developed for range independent environments and shows significant promise for future development.