Most published results show that area reduction of the finite-state machines (FSMs) is achieved by optimizing the state assignment. In order to minimize two-level and multilevel area of FSMs, an evolutionary strategy based state assignment, called ESSA, is proposed in this study. Two cost functions (i.e. Fitness functions) are defined for two-level and multilevel area minimization. A new selection strategy and a new mutation are proposed in HES, which are specifically designed based on the analysis of the search space and individual's distribution. The selection strategy sorts out parental individuals based on the crowding distance and fitness, and mutation uses 'replacement', '2-exchange' and 'shifting' operators, which is controlled by the hamming distance constraint, to generate offspring from the parental individuals. Experimental results show ESSA achieves a significant reduction of area to the previous publications in terms of number of cubes and literals in most benchmarks.