Asphalt binder performance grades (PGs) are important metrics in designing pavements for effective transportation infrastructure. The PG system relies on the binder’s stiffness and is determined through energy- and time-intensive physical testing. Physical properties, like stiffness, can also be determined by spin–lattice NMR relaxometry, a non-destructive chemical method. NMR relaxometry can quantify the molecular mobility of materials by determining relaxation times from exponential decays of excited nuclear magnetization. While relaxation times have been used to determine physical properties of materials, a quantitative relation to the PG grades of asphalt binder is yet to be established. In this study, T1 NMR relaxation analyses were used to differentiate between solid asphalt binders and determine the fastest yet still-reliable method of modeling exponential decay data. Algorithms that fit exponential decay relaxation data using one, two, or three independent relaxation times were compared with a 128-coefficient discrete inverse Laplace transformation to determine the best mathematical fit for a comparative analysis. The number of data points was then reduced from 256 to 64 to 16 and finally to 8 data points on a relaxation curve to reduce the testing time and determine the minimum number of data points needed for comparison. Two batches of PG 64-22 asphalt binder, along with samples of PG 76-22 and 94-10 binders, were investigated. The best compromise between measuring time and data reliability was found by acquiring 64 data points and then using a biexponential model to fit the experimental data. The PG 64-22 sources provided similar results, indicating similar physical properties. The PG 64-22 and PG 76-22 binders could also be compared via monoexponential data fits, but the PG 94-10 samples required an additional relaxation parameter for comparison. To differentiate all three binder grades, the primary relaxation times, along with their relative ratios, were utilized.