Cone crushers are critical units in the mineral and aggregate processing industries, where operational and design decisions significantly influence production efficiency and cost. This work builds upon our previous research on Discrete Element Method (DEM) simulations of a Hydrocone-type crusher using the GPU-based Demify® solver with an embedded cohesive zone BPM fracture model based on the work by Liu et al [1]. Prior results have demonstrated good predictive capabilities for key machine parameters (closed side setting, throw, speed) and responses (power, pressure, size distribution, wear). In this work, an optimization framework is applied for a parameterized cone crusher geometry to evaluate the optima for different objective functions relating to e.g. aggregate of mineral processing applications. We employ an asynchronous parallel surrogate optimization strategy (pySOT) [2] to seek an operational optimum under multi-objective considerations, such as throughput maximization, desired product size distribution, and wear minimization. The method identifies parameter trade-offs subject to power and pressure constraints. The optimization exercise is preceded by a statistical Design of Experiments (DOE) simulation study using a fractional factorial design approach in which a larger set of model and material parameters is evaluated from a sensitivity perspective. To improve the modelling framework, a simple approach to extrapolate the fine fraction below the minimum fragment element size is applied. Because the cohesive zone bonded element modelling approach inherently limits progeny sizes to the smallest mesh fragments, we propose a post-processing technique to extrapolate the product size distribution below that mesh-dependent minimum size. This hybrid approach preserves computational efficiency while achieving more realistic fine-end estimates of the final product. The mesh resolution is also investigated in relation to the fracture response on single particle and complete machine scale. This work demonstrates a comprehensive simulation-based framework for understanding and optimizing crushing machine performance. By integrating DOE-driven sensitivity analyses, surrogate model-based optimization, we achieve robust predictive capabilities for different objective functions related to different rock size reduction applications. The outcomes underline the continued potential of DEM-based tools for industrial comminution optimization, encouraging further refin