This study presents a data-driven optimization methodology applied to the continuous surface cap model (CSCM) to improve blast response prediction in reinforced concrete (RC) slabs. Focusing on three key tensile-related parameters-fracture energy, rate-effect onset, and rate-effect power-the optimization minimizes midspan deflection errors across three TNT charge levels. A two-stage global-to-local search reveals that joint tuning of these parameters eliminates the systematic bias often encountered when using auto-generated CSCM inputs in LS-DYNA. The optimized parameter set yields not only accurate deflection predictions but also realistic failure patterns. The results establish a validated and reusable optimization framework for improving blast simulations that involve fracture energy and strain-rate effects.