To improve the accuracy of Discrete Element Method (DEM) simulations for blast furnace materials, we present a structured calibration methodology for pellet and sinter particles based on nine key performance indicators (KPIs). For each material, the following process is followed: a Plackett-Burman sensitivity analysis to identify influential parameters, a Central Composite Design to develop polynomial regression models, and a comparison of multi-objective optimization techniques, including local optimization, genetic algorithms, and particle swarm optimization (PSO). This approach enables parameter input estimation of the DEM model. Experimental data is used to find input parameter values for the calibrated models, showing that PSO achieves faster convergence than GA and delivers accurate predictions at the original hopper height. However, discrepancies in the validation model suggest the need for refined parameter estimation to ensure robustness under varied conditions.