diff --git a/julia/src/inner_loop/nlp_solver.jl b/julia/src/inner_loop/nlp_solver.jl
index cad6fd1..83735aa 100644
--- a/julia/src/inner_loop/nlp_solver.jl
+++ b/julia/src/inner_loop/nlp_solver.jl
@@ -92,7 +92,7 @@ function solve_mission( guess::Mission_Guess,
                        "acceptable_constr_viol_tol" => 100tol,
                        "max_iter" => 10_000,
                        "max_cpu_time" => 300.,
-                       "print_level" => 5)
+                       "print_level" => 0)
   options = Options(solver=IPOPT(ipopt_options), derivatives=ForwardFD())
 
   x, _, info = minimize(optimizer!, x0, num_constraints, lower_x, upper_x, g_low, g_high, options)
diff --git a/julia/test/inner_loop/nlp_solver.jl b/julia/test/inner_loop/nlp_solver.jl
index 242c7c9..b4f98d8 100644
--- a/julia/test/inner_loop/nlp_solver.jl
+++ b/julia/test/inner_loop/nlp_solver.jl
@@ -1,4 +1,4 @@
-@testset "Phase" begin
+@testset "NLP Solver" begin
 
   using PlotlyJS: savefig
 
@@ -24,14 +24,13 @@
   p = plot(m, title="NLP Test Solution")
   savefig(p,"../plots/nlp_test_1_phase.html")
 
-  # Now we can look at a more complicated trajectory
+  # Now we can look at a slightly more complicated trajectory
   flybys = [Earth, Venus, Mars]
   launch_window = [DateTime(2021,10,1), DateTime(2021,12,1)]
   latest_arrival = DateTime(2023,1,1)
   dates = [DateTime(2021,11,1), DateTime(2022,3,27), DateTime(2022,8,28)]
   phases = Vector{Phase}()
   launch_v∞, _, tof1 = Thesis.lamberts(flybys[1], flybys[2], dates[1], dates[2])
-  println(launch_v∞)
   for i in 1:length(dates)-2
     v∞_out1, v∞_in1, tof1 = Thesis.lamberts(flybys[i], flybys[i+1], dates[i], dates[i+1])
     v∞_out2, v∞_in2, tof2 = Thesis.lamberts(flybys[i+1], flybys[i+2], dates[i+1], dates[i+2])
@@ -39,10 +38,33 @@
   end
   v∞_out, v∞_in, tof = Thesis.lamberts(flybys[end-1], flybys[end], dates[end-1], dates[end])
   push!(phases, Phase(flybys[end], v∞_in, v∞_in, tof, 0.01*ones(20,3)))
-  # This isn't quite right. V∞ discrepancy is too big
   guess = Mission_Guess(bepi, 12_000., dates[1], launch_v∞, phases)
   m = solve_mission(guess, launch_window, latest_arrival, verbose=true)
   p = plot(m, title="NLP Test Solution (2 Phases)")
   savefig(p,"../plots/nlp_test_2_phase.html")
 
+  # Here is the final, most complicated, trajectory to test
+  flybys = [Earth, Venus, Earth, Mars, Earth, Jupiter]
+  launch_window = [DateTime(2023,1,1), DateTime(2024,1,1)]
+  latest_arrival = DateTime(2031,1,1)
+  dates = [DateTime(2023,6,28),
+           DateTime(2023,10,30),
+           DateTime(2024,9,2),
+           DateTime(2025,2,10),
+           DateTime(2026,11,26),
+           DateTime(2029,10,16)]
+  phases = Vector{Phase}()
+  launch_v∞, _, tof1 = Thesis.lamberts(flybys[1], flybys[2], dates[1], dates[2])
+  for i in 1:length(dates)-2
+    v∞_out1, v∞_in1, tof1 = Thesis.lamberts(flybys[i], flybys[i+1], dates[i], dates[i+1])
+    v∞_out2, v∞_in2, tof2 = Thesis.lamberts(flybys[i+1], flybys[i+2], dates[i+1], dates[i+2])
+    push!(phases, Phase(flybys[i+1], v∞_in1, v∞_out2, tof1, 0.01*ones(20,3)))
+  end
+  v∞_out, v∞_in, tof = Thesis.lamberts(flybys[end-1], flybys[end], dates[end-1], dates[end])
+  push!(phases, Phase(flybys[end], v∞_in, v∞_in, tof, 0.01*ones(20,3)))
+  guess = Mission_Guess(bepi, 12_000., dates[1], launch_v∞, phases)
+  m = solve_mission(guess, launch_window, latest_arrival, verbose=true)
+  p = plot(m, title="NLP Test Solution (5 Phases)")
+  savefig(p,"../plots/nlp_test_5_phase.html")
+
 end