Knowledge discovery from data can be broadly categorized into two types: supervised and unsupervised. A supervised knowledge discovery process such as classification by decision trees typically requires class labels which are sometimes unavailable in datasets. Unsupervised knowledge discovery techniques such as an unsupervised clustering technique can handle datasets without class labels. They aim to let data reveal the groups (i.e. the data elements in each group) and the number of groups. For the ubiquitous task of clustering, K-Means is the most used algorithm applied in a broad range of areas to identify groups where intra-group distances are much smaller than inter-group distances. As a representative-based clustering approach, K-Means offers an extremely efficient gradient descent approach to the total squared error of representation; however, it not only demands the parameter k, but it also makes assumptions about the similarity of density among the clusters. Therefore, it is profoundly affected by noise. Perhaps more seriously, it can often be attracted to local optima despite its immersion in a multi-start scheme. We present an effective genetic algorithm that combines the capacity of genetic operators to conglomerate different solutions of the search space with the exploitation of the hill-climber. We advance a previous genetic-searching approach called GenClust, with the intervention of fast hill-climbing cycles of K-Means and obtain an algorithm that is faster than its predecessor and achieves clustering results of higher quality. We demonstrate this across a series of 18 commonly researched datasets.