This study is concerned with use of generalized linear mixed models (GLMM) to analyse the repeated measurements based on count data obtained from the sexual behaviors of male lambs. A combination of different optimization techniques and covariance structures were applied to four constructed models. These models were defined in terms of random effect specifications. Therefore, residuals was assumed to be random (Model A), intercept assumed to be random effect (Model B), time (slope) assumed to be random effect (Model C) and both intercept and time assumed to be random effects (Model D). Five different techniques quasi-newton (QUANEW), newton-raphson (NEWRAP), trust region (TRUREG), newton-raphson ridge (NRRIDG) and double-dogleg (DBLDOG) optimization techniques were used for analyzing these models. Three different covariance structures compound symmetry (CS), unstructured (UN) and first-order autoregressive (AR(1)) were used. In conclusion, based on likelihood criteria, the Model A with CS structure outperformed other models for the repeated measurement data of sexual behavior characteristics.