This paper presents a parameter tuning study of Differential Evolution (DE) algorithms, including standard DE as well as various adaptive DE algorithms (jDE, JADE and SHADE) for different computational budget scenarios. While there have been numerous parameter studies of DE for cheap computational budget scenarios where DE can utilize a relatively-large computational budget, there have been no previous studies of DE for expensive computational budget scenarios where only a small computational budget can be used for the search. Using the algorithm configuration tool SMAC, the DE variants are tuned independently for three different maximum number of evaluations: 10²×D, 10³×D and 10⁴×D evaluations, where D is the benchmark problem dimensionality. Each of these tuned parameter settings is then tested in each of the three different budget scenarios, which enables us to analyze the effect of both the tuning and testing phase on the performance of the tuned algorithm. For the parameter tuning phase, we use the CEC2014 benchmarks as training problems, and for the testing phase, we use all 24 problems from the BBOB noiseless benchmark set. The experimental results show: (1) the parameter settings obtained by SMAC vary significantly depending on the maximum number of evaluations on the training phase, (2) DE algorithms perform well when the computational budget on the training phase and the testing phase are similar, (3) the standard DE algorithm performs better than some adaptive DE algorithms for low-dimensional problems in expensive scenarios.