Clustering is an important technique used in discovering some inherent structure present in data. The purpose of cluster analysis is to partition a given data set into a number of groups such that data in a particular cluster are more similar to each other than objects in different clusters. Hierarchical clustering refers to the formation of a recursive clustering of the data points: a partition into many clusters, each of which is itself hierarchically clustered. Hierarchical structures solve many problems in a large area of interests. In this paper a new evolutionary algorithm for detecting the hierarchical structure of an input data set is proposed. Problem could be very useful in economy, market segmentation, management, biology taxonomy and other domains. A new linear representation of the cluster structure within the data set is proposed. An evolutionary algorithm evolves a population of clustering hierarchies. Proposed algorithm uses mutation and crossover as (search) variation operators. The final goal is to present a data clustering representation to find fast a hierarchical clustering structure.