A new hierarchical model predictive controller (HMPC) for autonomous vehicle steering control is presented. The controller generates a path of shortest distance by determining lateral coordinates on a longitudinal grid, while respecting road bounds. This path is then parameterized by arc length before being optimized to restrict the normal acceleration values along the trajectory's arc length. The optimized trajectory is then tracked using a nonlinear MPC scheme using a bicycle plant model to calculate an optimal steering angle for the tires. The proposed controller is evaluated in simulation during a double-lane-change maneuver, where it generates and tracks a reference trajectory while observing the road boundaries and acceleration limits. Its performance is compared to a controller without path optimization, along with another that uses a smooth, predetermined, reference path instead of creating its own initial reference. It is shown that the proposed controller improves the tracking compared to a controller without path optimization, with a four-times reduction in average lateral tracking error. The average lateral acceleration is also reduced by 6%. The controller also maintains the tracking performance of a controller that uses a smooth reference path, while showing a much greater flexibility due to its ability to create its own initial reference path rather than having to follow a predetermined trajectory.