When processing data, a principal aim is to maximize information inferred from a data set by minimizing the expected postprocessing uncertainties on parameters of interest. Nonlinear statistical experimental design (SED) methods can be used to find optimal trace profiles for processing amplitude-variation-with-angle (AVA) surveys that account for all prior petrophysical information about the target reservoir. Optimal selections change as prior knowledge of rock parameters and reservoir fluid content changes, and which of the prior parameters have the greatest effect on selected traces can be assessed. The results show that optimal profiles are far more sensitive to prior information about reservoir porosity than to information about saturating fluid properties. By applying ray-tracing methods, AVA results can be used to design optimal processing profiles from seismic data sets for multiple targets, each with different prior-model uncertainties.