Template matching (TM) plays an important role in several image processing applications such as feature tracking, object recognition, stereo matching and remote sensing. In a TM approach, it is sought the point in which it is proposed the best possible resemblance between a sub-image known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy. The simplest available TM method finds the best possible coincidence between the images through an exhaustive computation of the Normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy). In this chapter, a new algorithm based on the Electromagnetism-Like algorithm (EMO) is presented to reduce the number of search locations in the TM process. The algorithm uses an enhanced EMO version where a modification of the local search procedure is incorporated in order to accelerate the exploitation process. The number of NCC evaluations is also reduced by considering a memory which stores the NCC values previously visited in order to avoid the re-evaluation of the same search locations (particles). Conducted simulations show that the proposed method achieves the best balance over other TM algorithms, in terms of estimation accuracy and computational cost.