The main purpose of this thesis is to utilize landmarks with known coordinates and the distance between a target and landmarks to establish an objective function, and to optimize the objective function by using unconstrained direct search method to estimate the coordinate of target. A number of nodes in the sensor network serve as the landmarks according to landmark selection algorithm. Since the landmark selection algorithm is time-consuming, a simplified scheme that would improve the algorithm is to reuse the distance information that had been computed. Due to the limit of transmission range between nodes, utilizing the shortest-path distance estimation model can quickly estimate the distance between the target and non-adjacent landmarks. The main conception of the model is combining the manner of multi-hop with the shortest-path model. Due to the possible errors in distance estimation, the error per hop is considered for reducing the estimation errors. It will obviously reduce the localization errors of the target. The thesis utilizes unconstrained direct search method to optimize the objective functions such as the simplex evolutionary method (SEM), the cyclic coordinate method(CCM) and the Powell method (PM). CCM and PM will tackle the problem of finding the forward length along search direction. Hence, two schemes that combine CCM or PM with SEM are proposed to resolve the problem. Finally, simulations are conducted to generate random some nodes in an known area and to select landmarks from the nodes. Let the target be assigned in the area and do performance analysis of positioning algorithm. We discuss the performance of the positioning algorithm by considering the error per hop approach. We also discuss the effects on positioning by changing some variables such as the number of nodes, the number of landmarks and the transmission range of nodes. It is seen that the positioning errors will be reduced in examples where the number of landmark are four or the number of node are four hundred. The performance of positioning becomes accurate by reducing the distance estimation error.