The most common strategies would be:
- Repeated measures ANOVA with one within-subject factor (pre vs. post-test) and one between-subject factor (treatment vs. control).
- ANCOVA on the post-treatment scores, with pre-treatment score as a covariate and treatment as an independent variable. Intuitively, the idea is that a test of the differences between both groups is really what you are after and including pre-test scores as a covariate can increase power compared to a simple t-test or ANOVA.
There are many discussions on the interpretation, assumptions, and apparently paradoxical differences between these two approaches and on more sophisticated alternatives (especially when participants cannot be randomly assigned to treatment) but they remain pretty standard, I think.
One important source of confusion is that for the ANOVA, the effect of interest is most likely the interaction between time and treatment and not the treatment main effect. Incidentally, the F-test for this interaction term will yield exactly the same result than an independent sample t-test on gain scores (i.e. scores obtained by subtracting the pre-test score from the post-test score for each participant) so you might also go for that.
If all this is too much, you don't have time to figure it out, and cannot obtain some help from a statistician, a quick and dirty but by no means entirely absurd approach would be to simply compare the post-test scores with an independent sample t-test, ignoring pre-test values. This only makes sense if participants were in fact randomly assigned to the treatment or control group.
Finally, that's not in itself a very good reason to choose it but I suspect approach 2 above (ANCOVA) is what currently passes for the right approach in psychology so if you choose something else you might have to explain the technique in detail or to justify yourself to someone who is convinced, e.g. that “gain scores are known to be bad”.