In this study, a model predictive control (MPC) for parking control and obstacle avoidance is proposed. We transformed the nonlinear dynamics into two linear subsystems using time-state control form (TSCF). Both steering and travel range constraints can be considered using 1st order approximation with enough accuracy. The parking problem may include a switching movement where the switching point should be optimized. To resolve this problem, the switch back point can be determined automatically based on the framework of MPC. In addition, to realize the obstacle avoidance, we introduce an artificial potential field into the objective function of MPC. The performance of proposed method is verified through scale model car experiments of a parking problem with obstacles.