This work studies the optimization of scheduling of chassis and container movements at the operational level for individual trucking companies when Chassis Processing Facilities (CPFs) are available for use in the vicinity of a container port within a major metropolitan area. A multi-objective optimization problem is formulated in which the weighted combination of the total travel time for the schedules of all vehicles in the company fleet and the maximum work span across all vehicle drivers during the day is minimized. Time-varying dynamic models for the movements of chassis and containers are developed to be used in the optimization process. The optimal solution is obtained through a genetic algorithm, and the effectiveness of the developed methodology is evaluated through a case study which focuses on the Los Angeles/Long Beach port complex. The case study uses a trucking company located in the Los Angeles region, which can utilize three candidate CPFs for exchange of chassis. The company assigns container movement tasks to its fleet of trucks, with warehouse locations spread across the region. In the simulation scenarios developed for the case study, the use of CPFs at the trucking company level, can provide improvements up to 13% (depending upon the specific scenario) over the cases of not using any CPFs. It was found in this work that for typical cases where the number of jobs is much larger than the number of vehicles in the company fleet, the greatest benefit from CPF use would be in the cases where there are some significant job to job differences with respect to chassis usage.