Iterated local search (ILS) is used to construct the op-timal experimental designs for multi-dimensional constrained spaces, in which the inner loop is based on the stochastic co-ordinate-exchange (SCE) algorithm. Every time a local optimal solution is found by the SCE algorithm, the perturbation operat-or is applied to it, and then a new solution is explored in the areas where the exchange of coordinates may produce improve-ment, so as to retain the features and attributes of the current optimal solution and avoid the defects of random restart. We im-plement the iterated local coordinate-exchange algorithm for ex-perimental designs in the multi-dimensional constrained spaces. In addition, sensitivity analysis was conducted to analyze the im-pacts of the parameters on the performance of the proposed al-gorithm. Also we compared the performance of the proposed al-gorithm to the SCE algorithm using the random restart strategy. The analysis shows that the proposed algorithm is better than the SCE algorithm in terms of efficiency and quality, especially in the experimental designs for high-dimensional constrained space.