This study presents a methodology for assessing transit stop accessibility and optimizing stop placement using a synthetic population and network-based catchment areas. Traditional approaches often rely on static buffer zones, which fail to capture real-world variations in pedestrian access and transit connectivity. Our method dynamically assigns transit stop watersheds based on optimal routes to key destinations, accounting for diverse travel behaviors and multimodal connections. A synthetic population is integrated to refine demographic profiling within each catchment area, enabling a more accurate estimation of transit usage potential. By combining demographic data and network-based walking accessibility, we compute a continuous probability function for transit stop use, improving upon rigid distance thresholds. Additionally, our approach allows for iterative stop placement evaluation, recalculating catchment areas and usage likelihood scores to optimize transit network design. This framework provides a data-driven, user-friendly tool for planners to enhance transit accessibility and equitable service provision.