GLM Repeated Measures
If you have no missing data and everyone is measured at each of the 12 time points, then you could use
Analyze - GLM - Repeated Measures. This allows you to include a between subjects factor and the 12 time points. Your data would need to be set out in wide format with one SPSS variable for group and 12 SPSS variables for the 12 time points.
However, there are several issues with this approach. First, you are probably interested in trends over time and not whether the means for the 12 points differ. You could still run polynomial contrasts and examine the linear, quadratic and possibly cubic effects. You could examine how these interact with group to look at differential changes over time.
Then there is the issue of the structure of your residuals. Data measured over time tends to intercorrelate even after you take out any mean trend component.
Thus, you may want to look into linear mixed models. E.g., see here and here. This should give you control over how you want to model the effect of time and how you model the error structure in the data. You can also consider thinks like random effects.