Abstract The Extended Global Cardinality Constraint (EGCC) is an important component of constraint solving systems, since it is very widely used to model diverse problems. The literature contains many different versions of this constraint, which trade strength of inference against computational cost. In this paper, I focus on the highest strength of inference usually considered, enforcing generalized arc consistency (GAC) on the target variables. This work is an extensive empirical survey of algorithms and optimizations, considering both GAC on the target variables, and tightening the bounds of the cardinality variables. I evaluate a number of key techniques from the literature, and report important implementation details of those techniques, which have often not been described in published papers. Two new optimizations are proposed for EGCC. One of the novel optimizations (dynamic partitioning, generalized from AllDifferent) was found to speed up search by 5.6 times in the best case and 1.56 times on average, while exploring the same search tree. The empirical work represents by far the most extensive set of experiments on variants of algorithms for EGCC. Overall, the best combination of optimizations gives a mean speedup of 4.11 times compared to the same implementation without the optimizations. This paper is an extended abstract of the publication in Artificial Intelligence [Nightingale, 2011]. Constraint programming is a powerful and flexible means of solving combinatorial problems. Constraint solving of a combinatorial problem proceeds in two phases. First, the problem is modelled as a set of decision variables, and a set of constraints on those variables that a solution must satisfy. A decision variable represents a choice that must be made inorder to solve the problem. The domain of potential values associated with each decision variable corresponds to the options for that choice. Consider a sports scheduling problem, where each team ? This paper is an extended abstract of the AI Journal publi[Nightingale, 2011].