Collecting and analyzing ranking, rating, paired comparisons, or choices for optimal attribute-level combinations has a long tradition in marketing research (see e.g., Luce and Tukey (1964), Green and Rao (1969) Johnson (1974) and Louviere and Woodworth (1983)). A typical problem can be stated as follows: A focal firm wants to introduce new products (e.g., goods, services, or hybrid offers) into a market where own and/or foreign products already exists. However, the number of possible attribute-level combinations often is high and a complete enumeration by evaluating all possible product-lines is not feasible. In this paper, we apply the recent winners of comparisons with respect to accuracy and computation time – Ant Colony Optimization, Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing, Tabu Search – and two new solution methods – Cluster-based Genetic Algorithm and Max-Min Ant Systems – to 538 small- to large-size product-line design problems with up to 7.3310200 possible solutions. In this talk we discuss typical product-line problems formally. Then, an overview on proposed and applied solution methods follows. The talk closes with conclusions and an outlook.