The materialization of mu ltidimensional structures is a common way to speed up OLAP queries. Since there might be a huge number of those structures, a variety of proposals tried to select the most beneficial set, based on the profile of the queries and observing some constraints as materializing space and maintenance time, addressing a centralized storage facility. Only recently, the dist ributed scenario came to stage on this area, introducing the space dimension (and corresponding communication costs) into the equation to minimize costs. This paper focuses on the selection and allocation of distributed OLAP cubes, using evolutionary algorithms, having an extended aggregation lattice as framework to capture the distributed semantics. Moreover, the evaluation of the fitness of evolutionary solutions is based on cost estimation algorithms that simulate the execution of parallel tasks, using time units as cost metric.