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This paper presents a hierarchical trajectory planning based on the integration of a sampling and an optimization method for urban autonomous driving. To manage a complex driving environment, the upper behavioral trajectory planner searches the macro-scale trajectory to determine the behavior of an autonomous car by using environment models, such as traffic control device and objects. This planner infers reasonable behavior and provides it to the motion trajectory planner. For planning the behavioral trajectory, the sampling-based approach is used due to its advantage of a free-form cost function for discrete models of the driving environments and simplification of the searching area. The lower motion trajectory planner determines the micro-scale trajectory based on the results of the upper trajectory planning with the environment model. The lower planner strongly considers vehicle dynamics within the planned behavior of the behavioral trajectory. Therefore, the planning space of the lower planner can be limited, allowing for improvement of the efficiency of the numerical optimization of the lower planner to find the best trajectory. For the motion trajectory planning, the numerical optimization is applied due to its advantages of a mathematical model for the continuous elements of the driving environments and low computation to converge minima in the convex function. The proposed algorithms of the sampling-based behavioral and optimization-based motion trajectory were evaluated through experiments in various scenarios of an urban area.