Modern metropolitan areas rely heavily on urban rail networks to facilitate the movement of millions of commuters every day. While these systems provide efficient and sustainable mobility, they face persistent challenges, particularly overcrowding during peak hours and underutilization during off-peak periods. Overcrowding on transit vehicles is not only perceived as longer travel time but also imposes safety and security concerns for commuters. Past studies have explored strategies to minimizing overcrowding during peak hours and maximizing the utilization of the transit system during off-peak hours using strategies, such as collective incentive strategy, dynamic incentive pricing, peak-hour surcharge, or off-peak discounts, hybrid fare scheme, and flexible work start time. Studies have also reported that commuters are willing to change their departure time rather than switch their transportation mode to avoid congestion. The departure time flexibility in literature is modeled using the mixture of uniform and beta distribution for regular commuters. However, studies have not jointly considered departure time flexibility along with congestion pricing in a comprehensive behavioral framework. This study develops a data driven simulation-optimization framework that integrates passenger departure time flexibility and train scheduling to reduce onboard crowding while minimizing congestion-related surcharges in a commuter rail system. An agent-based simulation model is used to model individual passenger choices, where each agent selects departure times and trains based on preferred arrival times within departure time flexibility ranges, congestion-related surcharges, and perceived crowding. Smart card data are utilized to characterize passenger behavior and departure time flexibility distributions, and GTFS data is used to characterize the operational service environment.