Logistics optimization is essential for improving operational efficiency in industries reliant on transportation. This study focuses on the Pickup and Delivery Problem (PDP), an extension of the Vehicle Routing Problem (VRP), which involves both pickup and delivery tasks. Our research presents a multi-objective optimization approach to address PDPs, incorporating complexities such as alternative pick-up locations and time window constraints. We aim to minimize both delivery lateness and transportation costs using a Mixed-Integer Programming (MIP) model. Our methodology optimizes multiple objectives simultaneously by minimizing both delivery lateness and overall transportation costs. By developing novel approaches to improve logistics operations, this research contributes to advancing transportation optimization and supply chain management, ultimately leading to a more efficient and cost-effective logistics environment.