Real-valued optimization has attracted considerable interest because of its wide-ranging applications in areas such as control system design, circuit design, and hyperparameter tuning for machine learning. In this paper, we propose the skew multidimensional split-on-demand (smSoD), an extension of multidimensional split-on-demand (mSoD). mSoD serves as a discretization interface that enables discrete model-building genetic algorithms to tackle continuous-domain problems; smSoD further improves split-point selection by leveraging the sample distribution. We then embed smSoD into the integer version of the extended compact genetic algorithm (ECGA) and evaluate it on two benchmark suites: decomposable linkage problems and an extended version of the CEC2014 benchmark with additional subproblems. We compare smSoD+ECGA against bothmSoD+ECGA and L-SHADE. According to statistical tests, under high-dimensional scenarios, our method outperforms the other two methods on more than 50% of the test problems.