The k-truncated metric dimension of a graph is the minimum number of sensors (a subset of the vertex set) needed to uniquely identify every vertex in the graph based on its distance to the sensors, where the sensors have a measuring range of k. We give an algorithm with the goal that given any tree and any value for the measuring range of the sensors k, the algorithm finds the k-truncated metric dimension of that tree. The algorithm presented in this thesis is a modification of the algorithm given by Gutkovich and Song Yeoh [6]. The algorithm in this thesis improves on their algorithm in both validity and time complexity. We show that given any tree and any value k, the algorithm returns a k-resolving set for that tree. Moreover, we conjecture the difference in the k-truncated metric dimension of any tree and the size of the k-resolving set found by the algorithm for that tree is never greater than one. The time complexity of the algorithm is proven to be O(k3n), where k is the measuring range of the sensors and n is the number of vertices in the tree. This implies that the time complexity is linear in n for fixed k.