Commercial computer games has become one of the largest industries in the area of entertainment. Real Time Strategy (Rts) based games is one of the most important among different types of computer games and is also considered to be a good research platform within Artificial Intelligence (Ai). There exists a number of algorithms that can deal with the Ai related problems of this domain, e.g. the ones of getting as cheap or fast as possible from one point to another (i.e. path planning). However most of the algorithms used in academia are better suited for problems that do not need to be solved within tight time frames. Anytime algorithms (Aa) are algorithms that can be stopped at any point in time and yet come up with a preliminary solution. We believe that by making algorithms anytime, we can optimize their behaviors to better suit the Rts domain. This study will introduce a tool for evaluating path planning algorithms and compare the performances of A*, Recursive Best First Search (Rbfs), Potential Fields (Pf) and their anytime versions for the path planning problem.