This article explains the single-search-based heuristics for multiobjective optimization. The most prominent heuristics of multiobjective simulated annealing, multiobjective tabu search, and Pareto local search are each explained with a detailed pseudocode and further summaries of algorithmic ideas. This is followed by a brief review of hybrid heuristics combining features of single search and population-based heuristics. The article ends with brief conclusions and suggestions for further reading.