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Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. However, it cannot guarantee finding the most optimal path. A recently proposed extension of RRT, known as Rapidly Exploring Random Tree Star (RRT*), claims to achieve convergence towards the optimal solution but has been proven to take an infinite time to do so and with a slow convergence rate. To overcome these limitations, we propose an extension of RRT*, called RRT*-Smart, which aims to accelerate its rate of convergence and to reach an optimum or near optimum solution at a much faster rate and at a reduced execution time. Our novel algorithm inculcates two new techniques in RRT*: these are path optimization and intelligent sampling. Simulation results presented in various obstacle cluttered environments confirm the efficiency of RRT*-Smart.