Optimizing contact pressure in a biomonitoring garment system is crucial to improving signal quality by reducing skin impedance and motion artifacts. Building upon previous research, which introduced a strategic methodology for enhancing electrocardiogram (ECG) biosignal quality through material selection and pattern sizing guided by a developed simulation-based contact pressure prediction model (CP model), this study investigates the model's efficacy across varied knits (plain, interlock, plaited single jersey, and plaited interlock) and yarn filament densities to design a more complex ECG chest band. In this study, our CP model demonstrated strong predictive capabilities with R-squared values exceeding 0.87, which are compatible with physical uniaxial tensile test-based prediction showing an R-squared value of 0.88. Our selected appropriate knit substrates (single jersey and interlock plaiting knit) for pattern reduction values of 20 and 5%, respectively, for designing ECG elastic chest bands result in enhanced biosignal quality with signal-to-noise ratios (SNRs) of 42.85 (±0.08) and 40.92 (±0.06), respectively, comparable to the wet electrode with an SNR of 40.02 (±0.32). This study confirms that selected appropriate materials and patterns can significantly enhance ECG signal quality by optimizing contact pressure to the ideal range of at least 0.53 to 1.05 kPa under the chest area, as demonstrated with a female subject. These findings provide valuable insights into using textile-based electrodes in garment designs by strategically engineering contact pressure to mitigate motion artifacts with the CP model and simulation technique.