In this paper we propose a new method to obtain transition rules of one-dimensional two-state cellular automata (CAs) using genetic algorithms (GAs). CAs have the advantages of producing complex systems from the interaction of simple elements, and have attracted increased research interest. However, the difficulty of designing CAs' transition rules to perform a particular task has severely limited their applications. The evolutionary design of CA rules has been studied by the EVCA group in detail. A GA was used to evolve CAs for two tasks: density classification and synchronization problems. That GA was shown to have discovered rules that gave rise to sophisticated emergent computational strategies. Sipper has studied a cellular programming algorithm for 2-state non-uniform CAs, in which each cell may contain a different rule. Meanwhile, Land and Belew proved that the perfect two-state rule for performing the density classification task does not exist. However, Fuks´ showed that a pair of human written rules performs the task perfectly when the size of neighborhood is one. In this paper, we consider a pair of rules and the number of rule iterations as a chromosome, whereas the EVCA group considers a rule as a chromosome. The present method is meant to reduce the complexity of a given problem by dividing the problem into smaller ones and assigning a distinct rule to each one. Experimental results for the two tasks prove that our method is more efficient than a conventional method. Some of the obtained rules agree with the human written rules shown by Fuks´. We also grouped 1000 rules with high fitness into 4 classes according to the Langton's λ parameter. The rules obtained by the proposed method belong to Class- I, II, III or IV, whereas most of the rules by the conventional method belong to Class-IV only. This result shows that the combination of simple rules can perform complex tasks.