State-of-the-art design of analog and radio frequency integrated circuits is often accomplished using sizing optimization. In this paper, an innovative combination of principal component analysis (PCA) and evolutionary computation is used to increase the optimizer’s efficiency. The adopted NSGA-II optimization kernel is improved by applying the genetic operators of mutation and crossover on a transformed design-space, obtained from the latest set of solutions (the parents) using PCA. By applying crossover and mutation on variables that are projections of the principal components, the optimization moves more effectively, finding solutions with better performances, in the same amount of time, than the standard NSGA-II optimization kernel. The proposed method was validated in the optimization of two widely used analog circuits, an amplifier and a voltage controlled oscillator, reaching wider solutions sets, and in some cases, solutions sets that can be almost 3 times better in terms of hypervolume.