In this paper, a novel method of constraining the space of input variables is introduced in case of potentially-destructive electrical measurements. Contrary to traditional sweep-based measurement execution, the method enables safe evaluation of samples lying on arbitrary grids. To achieve that, the constraints are modeled as Gaussian processes, which allow to predict the constraint value and estimate the model uncertainty at the given sample. The further a candidate point lies from the samples, for which measurement results are available, the higher the uncertainty. Since the initial models are usually inaccurate, we update them and their uncertainty predictions each time new measurement results are available. Transistor measurements on several uniform random experimental designs prove that the proposed method can efficiently identify the safe operating area with the error rate not exceeding 2.5%. Few constraint violations found were not severe and no degradation in transistor performance was observed after the measurement campaign.