Summary: This paper describes the development and application of distributed evolution strategies (ES) in the field of mixed-discrete structural optimization. ES are direct, probabilistic optimization methods based on the use of the three evolution operators recombination, mutation and selection in an evolving population of competing individuals in the design space. Advanced features of the employed ES include self-adaptation of strategy parameters to achieve on-line tuning of the optimization process, and a flexible selection scheme that allows scalable lifetimes of individuals. The property of using a population of coexisting but independent individuals allow an efficient realization in distributed computing environments. This approach results in a drastic reduction of response time and in an improved applicability to large-scale problems.