In this paper we develop and test experiment methodologies for selection of the best alternative among a discrete number of available treatments. We consider a scenario where a researcher sequentially decides which treatments are assigned to experimental units. This problem is particularly important if a single measurement of the response to a treatment is time-consuming and there is a limited time for experimentation. This time can be decreased if it is possible to perform measurements in parallel. In this work we propose and discuss extensions of the standard sequential Knowledge Gradient policy to allow for parallelized allocation of measurements. The two parallelization scenarios we consider are respectively assuming synchronous and asynchronous allocation of the measurements. Computer simulation of our algorithms indicates that the considered experimentation algorithms can yield significant benefits in terms of the time needed to obtain a desired approximation of the best solution.