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Cache memory optimization has an important impact on the energy consumption of the embedded system. However, optimization is a hard task due to the large exploration space and conflicting objectives. In this work five multiobjective optimization techniques are applied to cache memory optimization. The PESA-II, NSGAII, SPEA2, PAES and NPGA approaches were applied to 18 different applications from MiBench and PowerStone benchmark suites. Results compared the quality of results in terms of the metrics of general distance, diversity, hypervolume and precision. All techniques had good performance to cache optimization, but PESA-II showed a better performance for all metrics analyzed, having better results in 83% and 88% of cases, compared with the metrics of generational distance and hypervolume, respectively. Additionally, PESA-II needs to explore only 1.47% of exploration space, finding solutions near to Pareto Optimal. [ABSTRACT FROM AUTHOR]