Evolutionary algorithms (EA) have proved to be well suited for optimization problems with multiple objectives. Due to their inherent parallelism they are able to capture a number of solutions concurrently in a single run. In this report, we propose a new evolutionary approach to multicriteria optimization, the Strength Pareto Evolutionary Algorithm (SPEA). It combines various features of previous multiobjective EAs in a unique manner and is characterized as follows: a) besides the population a set of individuals is maintained which contains the Pareto-optimal solutions generated so far; b) this set is used to evaluate the fitness of an individual according to the Pareto dominance relationship; c) unlike the commonly-used fitness sharing, population diversity is preserved on basis of Pareto dominance rather than distance; d) a clustering method is incorporated to reduce the Pareto set without destroying its characteristics. The proof-of-principle results on two problems suggest that SPE