An algorithm featuring multiple local search operators and multiple neighborhood structures is applied to the uncapacitated exam proximity problem. The use of multiplicity is to enable effective interplay between intensification and diversification during the search process. The algorithmic design is inspired by Hansen and Mladenovic’s Variable Neighborhood Descent algorithm and the “one operator, one landscape” point of view. Its performance was evaluated using publicly available datasets. For the uncapacitated exam proximity problem, the multi-neighborhood and multi-operator algorithm compared favorably against other search techniques. These results should encourage further research on the application of multiple operator approach as solution techniques to the uncapacitated exam proximity problem.