Target tracking problem has many practical applications in real life. In submarines, target tracking is done using, preferably, passive sensors. These sensors measure only the bearing angles between the observed target and the ownship. Therefore, this problem is generally referred as bearing only target tracking or target motion analysis. The classical approach is to use a state observer based filter, i.e. Extended Kalman Filter, to estimate the range, course and speed of the target, using only the bearings. In recent studies, the problem is solved as a global optimization problem by utilizing evolutionary algorithms with respect to some objective functions. In this study, we investigate the effect of the commonly used cost functions on the performance of the TMA algorithms. Particularly, we investigate the cost functions based on bearing differences and equidistant line segments. The simulation results show that the latter gives a sub-optimal solution to the target motion analysis problem, compared to the former.