This work proposes a novel framework for motion planning using trajectory optimization for autonomous driving. First, a two-phase behavioral policy maker (BPM) is proposed as a high-level decision maker to mimic human-like driving style by avoiding unnecessary tasks and early lane changes. Second, a comprehensive study on iterative adaptive weight tuning functions has been done to limit manual weight tuning in the Constrained Iterative Linear Quadratic Regulator (CILQR) motion planner. Third, a jerk-minimized CILQR is presented to ensure the comfort and safety of passengers by generating smooth trajectories. The simulation results show efficiency, safety, and comfort of generated trajectories.