Grabs are often used for unloading bulk carriers that transport iron ore cargoes around the globe. The unloading process is time-consuming, and terminals strive to maximise their turnover capacity. Grab performance is a combination of maximum crane capacity, grab design and bulk material behaviour. Due to bulk uncertainties occurred by varying physical bulk properties, moisture content and consolidation, grab performance is hard to predict. To incorporate the bulk variability in the design process of grabs, or other large-scale bulk handling equipment, an optimization framework is proposed in which equipment is systematically optimized. With the framework an iron ore grab is optimized for handling iron ore pellets and iron ore fines. By a minimum number of experiments grab design was improved, increasing the turnover capacity for iron ore pellets by 12% and iron ore fines with 8%, and reducing the effect of varying bulk conditions. Optimization methods such as Latin Hypercube sampling,surrogate modelling and genetic algorithms proved to be successful for grab optimization. Further research of implementing the framework in the design process, and surrogate modelling is recommended.