We examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a three-form design at each wave such that 25% of data are missing. The second is wave-level planned missingness such that each participant is missing up to two waves of data. The third combines both forms of missingness. We find that three-form missingness results in high convergence rates, little parameter estimate or standard error bias, and high efficiency relative to the complete data design for almost all parameter types. In contrast, wave missingness and the combined design result in dramatically lowered efficiency for parameters measuring individual variability in rates of change (e.g., latent slope variances and covariances), and bias in both estimates and standard errors for these same parameters. We conclude that wave missingness should not be used except with large effect sizes and very large samples.