The way of clonal selection algorithm randomly generated population that will easily lead to numbers of non uniform distribution of values in the solution space, thus increasing the data redundancy phenomenon.To overcome the shortcomings of clonal selection algorithm, a chaotic clonal optimization algorithm for function optimizing is proposed by combining clonal selection algorithm, chaos optimization. This algorithm uses chaotic characteristics randomness, ergodicity and regularity to avoid trapping around local optimal. Equivalent division strategy is introduced by reducing the possible data redundancy phenomenon. The simulation results show that the proposed algorithm can converge to the global optimum at quicker rate in a given range.