Abstract: The hysteresis characteristics of the magnetic core fundamentally dictate the performance limits of fluxgate sensors. To address the lack of standardized comparative data and the scarcity of Jiles-Atherton (J-A) model parameters required for high-fidelity simulations, this study establishes an integrated evaluation framework linking hysteresis parameter identification to sensor performance simulation. First, Sobol global sensitivity analysis is employed to quantify the governing mechanisms of microscopic parameters on macroscopic magnetic properties. Second, a physics-constrained hybrid optimization algorithm is proposed for J-A parameter identification, achieving a computational efficiency improvement of approximately 16 times compared to traditional differential evolution methods. Subsequently, a finite element simulation platform for fluxgate sensors is developed. A simulation parameter library covering seven mainstream soft magnetic materials is constructed, with its predictive capability validated against experimental data from the literature. Leveraging this library, the sensitivity characteristics of different materials are quantitatively compared under unified excitation conditions. Furthermore, the study reveals a convergence trend in size-dependent attenuation rates for materials with varying permeabilities during core miniaturization. This work provides a quantitative tool for the material selection and structural optimization of fluxgate sensors.